< Back Tamara Broderick, PhD WiML Director (2013-2019)

< Back Hanna Wallach, PhD WiML Co-Founder, President (2009-2012), Director (2012-2016) Visit my Profile

What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. Check out our events page for up-to-date listings of events. Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning. How can I join the WiML mailing list? Join the mailing list directly here. What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list. How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker/panelist/area chair/program committee member/ etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list. I want to circulate a job posting. Can WiML help me? Post directly to our mailing list. How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list. Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list. Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org. And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here! I have a question that isn't here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list. Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! FAQ

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 16th Women in Machine Learning Workshop (WiML 2021) The 16th WiML Workshop is co-located with virtual NeurIPS on Thursday, December 9th and Friday, December 10th, 2021. Speakers Logistics Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications, it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning. This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. Now in its 16th year, the 2021 workshop is co-located virtually with NeurIPS . Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch at ICML and AAAI conferences, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Sunita Sarawagi Professor, Indian Institute of Technology Bombay Meire Fortunato Research Scientist, Deepmind Adriana R Soriano Research Scientist, Facebook AI Research Adjunct Professor, McGill University Bo Li Assistant Professor, University of Illinois at Urbana–Champaign Jade Abbott Machine Learning Lead, Retro Rabbit Orevaoghene Ahia PhD Student, University of Washington Perez Ogayo Master Student, Carnegie Mellon University Location This workshop will be virtual. WiML Platform This year WiML would be on GatherTown platform. For detailed instruction, please check: https://www.gather.town/ PROGRAM PANELISTS MENTORSHIP ROUNDTABLES SPONSOR EXPO SOCIAL ACCEPTED POSTERS Thursday, December 9, 2021 WiML Workshop 1 (UTC time in 24 hour format) 2:00 – 3:00 Pre-Workshop Informal Social 3:00 – 3:20 Opening Remarks – WiML 2021 organizers 3:20 – 3:30 WiML D&I Chairs Remarks 3:30 – 4:15 Invited talk – Machine Learning as a Service: The Challenges of Serving diverse client Distributions, Sunita Sarawagi 4:15 – 4:40 Contributed talk #1 – Regret minimization in heavy-tailed bandits, Shubhada Agrawal 4:45 – 5:45 Poster Session #1 5:45 – 6:15 Break 6:15 – 7:00 Invited talk – Learning physics models that generalize, Meire Fortunato Friday, December 10, 2021 WiML Workshop 2 (UTC time in 24 hour format) 2:00 – 3:00 Speed Networking/Social 3:00 – 4:00 Social in Gather Town 4:00 – 5:05 Invited talk – The Unreasonable Effectiveness of Collaborative Research - The Masakhane Story, Jade Abbott, Perez Ogayo, Orevaoghene Ahia 5:05 – 5:30 Contributed talk #2 – Syntax-enhanced Dialogue Summarization, Seolhwa Lee 5:30 – 7:00 Social in Gather Town WiML Workshop 3 (UTC time in 24 hour format) 10:00 – 11:00 Speed Networking/Social 11:00 – 12:45 Mentorship Roundtables I | Sponsor Expo 12:45 – 13:45 Poster Session #2 | Sponsor Expo 13:45 – 13:50 Break 13:50 – 14:35 Invited talk – Seeing the unseen: Inferring unobserved information from limited sensory data, Adriana Romero-Soriano 14:35 – 15:00 Contributed talk #3 – Causal Meta-learning by Making Informative Interventions about the Functional Form Chentian Jiang WiML Workshop 4 (UTC time in 24 hour format) 19:00 – 19:45 Invited talk – Trustworthy Machine Learning via Logic Inference, Bo Li 19:45 – 20:10 Contributed talk #4 – A Graph Perspective on Neural Network Dynamics Fatemeh Vahedian 20:10 – 20:15 Break 20:15 – 21:55 Mentorship Roundtables II | Sponsor Expo 21:55 – 22:40 Panel Discussion: Career and Life 22:40 – 23:00 Closing Remarks Emily Denton Research Scientist Google Devi Parikh Research Director at Facebook AI Research & Associate Professor at Georgia Tech Adriana R Soriano Research Scientist, Facebook AI Research Adjunct Professor, McGill University Bo Li Assistant Professor, University of Illinois at Urbana–Champaign All participants are required to abide by the WiML code of conduct . Joint Affinity Groups Poster Session This poster session will be held jointly with other affinity workshops including Black in AI , LatinX in AI , Queer in AI , and Indigenous in AI . Poster IDs: J.001—J.190 Time: Tuesday Dec 7, 5:00 - 7:00 UTC Location: Joint Affinity Groups Poster Session Gather.Town WiML Poster Session #1 Poster IDs: W001—W040 Time: Thursday Dec 9, 4:45 - 5:45 UTC Location: WiML Gather.Town Poster Rooms 1 & 2 A listing of posters presented in this session can be found here . WiML Poster Session #2 Poster IDs: W041—W099 Time: Friday Dec 10, 12:45 - 13:45 UTC Location: WiML Gather.Town Poster Rooms 3, 4, & 5 A listing of posters presented in this session can be found here . WiML Poster Session #1: Poster Room 1 (W001 - W019) Identifying Hijacked Reviews Monika M Daryani*; James Caverlee Polaris: accurate spot detection for biological images with deep learning and weak supervision Emily C Laubscher*; Nitzan Razin; Will Graf; David Van Valen Feedforward Omnimatte Sharon Zhang*; Jonathan Huang; Vivek Rathod Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies Anaelia Ovalle* Identifying ATT&CK Tactics in Android Malware Control Flow Graph Through Graph Representation Learning and Interpretability Christine Patterson*; Jeffrey Fairbanks; Andres Orbe; Edoardo Serra; Marion Scheepers Self-Supervised Visual Representation Learning for Time-series Clustering Gaurangi Anand*; Richi Nayak A Data-driven Approach to Infer Latent Dynamics of COVID-19 Transmission Model Sujin Ahn*; Minhae Kwon Soil Moisture Estimation with cycleGANs for Time-series Gap Filing Natalia Efremova*; Mohamed-el-amine Seddik; Esra Erten Automated deep lineage tree analysis using a Bayesian single cell tracking approach Kristina Ulicna*; Giulia Vallardi; Guillaume Charras; Alan R Lowe Evaluating the Impact of Embedding Representations on Deception Detection Ellyn Ayton*; Maria Glenski SPP-EEGNET: An Input-Agnostic Self-supervised EEG Representation Model for Inter-Dataset Transfer Learning Xiaomin Li*; Vangelis Metsis Across the Pond and Back: Evaluation of News Deception Detection Approaches Across Natural and Synthetic Regional Dialects Robin Cosbey*; Maria Glenski Graph Convolutional Networks for Multi-modality Movie Scene Segmentation Yaoxin Li*; Alexander Wong; Mohammad Javad Shafiee Data Efficient Domain Adaptation using FiLM Sinjini Mitra*; Ankita Shukla; Rushil Anirudh; Jayaraman Thiagarajan; Pavan Turaga Deep Generative Models for Task-Based fMRI Analysis Daniela F de Albuquerque*; Jack Goffinet; Rachael Wright; John M Pearson Active Noise Cancellation for Spatial Computing Li Chen*; Purvi Goel; David Yang; Xiang Gao; Ilknur Kaynar Kabul Self-Supervision for Scene Graph Embeddings Brigit Schroeder*; Adam M Smith; Subarna Tripathi A Vision-Based Gait Analysis Framework for Predicting Multiple Sclerosis Rachneet Kaur*; Manuel Hernandez; Richard Sowers TaxonBags: Clustering and Vote for Precise Metagenomic Taxonomic Classification Induja Chandrakumar* WiML Poster Session #1: Poster Room 2 (W026 - W047) Gaussian Process Bandits with Aggregated Feedback Mengyan Zhang*; Russell Tsuchida; Cheng Soon Ong Privacy-Preserving Federated Multi-Task Linear Regression: A One-shot Linear Mixing Approach Inspired by Graph Regularization Harlin Lee* Comparative Analysis of Machine Learning Techniques for Breast Cancer Detection Jesutofunmi O Afolayan* Drought and Nitrogen Induced Stress Identification for Maize Crop using Deep Learning deployed on Unmanned Aerial Vehicles (Drones) Tejasri Nampally*; Ujwal Sai; Siddha Ganju; Ajay Kumar; Rajalakshmi Pachamuthu; Balaji Naik Banothu Scene statistics and noise determine the relative arrangement of receptive field mosaics Na Young Jun* Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks Yujun Yan*; Milad Hashemi; Kevin Swersky; Yaoqing Yang; Danai Koutra Learning Aerodynamics and Instrument behavior to Fly in Dangerous Conditions Cynthia Koopman*; David Zammit Mangion; Alexiei Dingli Reading the Road: Leveraging Meta-Learning to Learn Other Driver Behavior Anat Kleiman*; Ryan P Adams Commit-Checker: A human-centric approach for adopting bug inducing commit detection using machine learning models Naz Zarreen Oishie*; Banani Roy Using Embeddings to Estimate Peer Influence on Social Networks Irina Cristali*; Victor Veitch Mitigating Overlap Violations in Causal Inference with Text Data Lin Gui*; Victor Veitch Automatic Curricula via Expert Demonstrations Siyu Dai*; Andreas Hofmann; Brian Williams Leveraging Resource Allocation and Approximation for Faster Hyperparameter Exploration Shu Liu* The Many Hats We Wear as Machine Learning Practitioners for Marine Mammal Conservation Louisa van Zeeland*; Gracie Ermi Exploiting Hyperdimensional Computing and Probabilistic Inference for Reasoning Across Levels of Abstraction in Dynamic Biosignal-Based Applications Laura I Galindez Olascoaga*; Alisha Menon; Jan M. Rabaey Augment Your Deterministic Model with Monte Carlo Dropout to Combat Noisy Labels Li Chen*; Karen Chen; Purvi Goel; Ilknur Kaynar Kabul Occlusion-Aware Crowd Navigation Using People as Sensors Ye-Ji Mun*; Masha Itkina; Katherine Driggs-Campbell Physics-assisted Machine Learning Abhilasha Katariya*; Jin Ye; Dipal Gupta; Rohit Malshe; Chinmoy Mohapatra; Gautam Natarajan; Liron Yedidsion An Interpretable Approach to Hateful Meme Detection Tanvi M Deshpande*; Nitya Mani Solving the super rural and super dense delivery with asset-light programs Jin Ye*; Dipal Gupta; Abhilasha Katariya; Rohit Malshe; Natarajan Gautam; Liron Yedidsion; Chinmoy Mohapatra Model-Free Learning for Continuous Timing as an Action Helen Zhou*; David Childers; Zachary Lipton Accelerating Symmetric Rank 1 Quasi-Newton Method with Nesterov's Gradient Indrapriyadarsini Sendilkkumaar*; Shahrzad Mahboubi; Hiroshi Ninomiya; Takeshi Kamio; Hideki Asai WiML Poster Session #2: Poster Room 3 (W050 - W071) Generating Thermal Human Faces for Physiological Assessment using Thermal Sensor Auxiliary Labels Catherine Ordun*; Sanjay Purushotham; Edward Raff Machine Learning API in NASA’s WorldView Satellite Image Search System Kai E Priester*; Daniela Fragoso Syntax-enhanced Dialogue Summarization using Syntax-aware information Seolhwa Lee*; Kisu Yang; Chanjun Park; João Sedoc; Heuiseok Lim COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification Audrey Chung*; Mahmoud Famouri; Andrew Hryniowski; Alexander Wong Predictive classification of clinical ball catching trials with recurrent neural networks Jana Lang* Effectiveness of Transformers on Session-Based Recommendation Sara Rabhi*; Ronay Ak; Gabriel S P Moreira; Jeong Min Lee; Even Oldridge The impact of weather information on machine-learning probabilistic electricity demand predictions Yifu Ding*; Hannah Bloomfield; Malcolm McCulloch Social Representation of Political Inclination of Users Anjali Jha* Combining semantic search and twin product classification for recognition of purchasable items in voice shopping Dieu Thu Le; Verena Weber*; Melanie Bradford Sequential Decision Making with Limited Resources Hallee E Wong*; Maggie Makar; Aniruddh Raghu; John Guttag As easy as APC: Leveraging self-supervised learning in the context of time series classification with varying levels of sparsity and severe class imbalance Fiorella Wever*; Laura Symul; Victor Garcia; T. Anderson Keller Machine learning powered quantitative histologic assessment of disease severity in ulcerative colitis Kathleen Sucipto*; Archit Khosla; Fedaa Najdawi; Michael Drage; Maryam Pouryahya; Stephanie Hennek; Victoria Mountain; Murray Resnick; Amaro Taylor-Weiner; Deepta Rajan; Ilan Wapinski; Andy Beck Improved robustness to disfluencies in RNN-Transducer based Speech Recognition Tina Raissi*; Valentin Raissi; Manuel Giollo; Guglielmo Camporese ``We Don't Talk Anymore?": An analysis of cross-cutting political discussion on Reddit Dulshani Withana Thanthri Gamage* A Graph Perspective on Neural Network Dynamics Fatemeh Vahedian*; Ruiyu Li; Puja Trivedi; Di Jin; Danai Koutra How we browse: Measurement and analysis of digital behavior Yuliia Lut*; Michael Wang; Elissa M. Redmiles; Rachel Cummings Topological characterizations of neuronal fibers and its implications in comparing brain connectomes S.* Shailja; B.S. Manjunath Towards Automated Evaluation of Explanations in Graph Neural Networks Vanya BK*; Balaji Ganesan; Aniket Saxena; Devbrat Sharma; Arvind Agarwal Graph Representation Learning on Trajectory-Encoded Volumetric Heatmaps for Human Motion Generation Michelle Wu*; Zhidong Xiao; Hammadi Nait-charif Opening the Black Box: High-dimensional Safe Policy Search via Sim-to-real Aneri Muni* , Matteo Turchetta, Andreas Krause Transformer-based Self-Supervised Learning for Medical Images Mariia Dobko*; Mariia Kokshaikyna Fixed Neural Network Steganography: Train the images, not the network Varsha Kishore*; Xiangyu Chen; Yan Wang; Boyi Li; Kilian Weinberger WiML Poster Session #2: Poster Room 4 (W078 - W088) Regret Minimization in Heavy-Tailed Bandits Shubhada Agrawal*; Sandeep K Juneja; Wouter M Koolen Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning Siddha Ganju*; Sayak Paul Classification of Shoulder Impingement Syndrome using Transfer Learning model Raquel Marasigan* Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation Hanjie Chen*; Yangfeng Ji A Neural Network Ensemble Approach to System Identification Elisa Negrini*; Giovanna Citti; Luca Capogna Predicting Fake News and Real News Spreaders' Influence Amy Zhang*; Daniel Hammer; Aaron Brookhouse; Francesca Spezzano; Liljana Babinkostova Parkinson’s Disease Detection using Imputed Multimodal Datasets Hetvi Jethwani*; Bhumika Chopra How Much Data Analytics is Enough?: The ROI of Machine Learning Classification and its Application to Requirements Dependency Classification Gouri Deshpande*; Guenther Ruhe; Chad Saunder Propagation on Multi-relational Graphs for Node Regression Eda Bayram* Do You See What I See: Using Augmented Reality and Artificial Intelligence Shruti Karulkar*; Louvere Walker-Hannon; Sarah Mohamed Strategic Clustering Ana-Andreea Stoica*; Christos Papadimitriou WiML Poster Session #2: Poster Room 5 (W106 - W130) Interpretable Machine Learning with Symbolic Regression Aurélie Boisbunon*; Carlo Fanara; Ingrid Grenet; Jonathan Daeden; Alexis Vighi; Marc Schoenauer Clipping Range Methods in Proximal Policy Optimization Mónika Farsang* The Two-sample Problem in High Dimension: A Ranking-based Method Myrto Limnios*; Stephan Clémençon; Nicolas Vayatis Causal meta-learning by making informative interventions about the functional form Chentian Jiang*; Chris Lucas Maintenance planning framework using online and offline deep reinforcement learning Zaharah Bukhsh*; Nils Jansen; Hajo Molegraaf Machine Learning-based Mobility Assessment from Passively Sensed Digital Biomarkers Emese Sükei*; Pablo M Olmos; Antonio Artés Getting Started with Model Cards Maitreyi Chitale*; Anoush Najarian; Helen Chigirinskaya; Sindhuja Parimalarangan; Louvere Walker-Hannon; Rajasi Desai; Kyle Rawding; Brian Liu Interpretable & Hierarchical Topic Models using Hyperbolic Geometry Simra Shahid*; Tanay Anand; Sumit Bhatia; Nikaash Puri; Balaji Krishnamurthy; Nikitha Srikanth Fairness properties do not transfer: do we have viable solutions for real-world applications? Jessica Schrouff*; Natalie GHarris; Sanmi Koyejo; Ibrahim Alabdulmohsin; Eva Schnider; Krista Opsahl-Ong; Alex Brown; Subhrajit Roy; Diana Mincu; Christina Chen; Awa Chen; Yuan Liu; Vivek Natarajan; Katherine Heller; Alexander D'Amour Combining Transfer Learning And Transformer Attention Mechanism to Increase Aqueous Solubility Prediction Performance Magdalena Wiercioch*; Johannes Kirchmair Application of an interpretable graph neural network to predict gene expression in histopathological images Ciyue Shen*; Collin Schlager; Deepta Rajan; Victoria Mountain; Ilan Wapinski; Amaro Taylor-Weiner; Maryam Pouryahya; Robert Egger Self-supervised pragmatic reasoning Jennifer Hu*; Roger Levy; Noga Zaslavsky AI-Driven Predictive Analytics to Inform Nuclear Proliferation Detection in Urban Environments Anastasiya Usenko*; Joonseok Kim; Ellyn Ayton; Svitlana Volkova Measuring the Cause and Effect in Scientific Productivity: A Case Study of the ACL Community Jasmine R Eshun*; Maria Glenski; Svitlana Volkova Scalable Bayesian Network Structure Learning with Splines Charupriya Sharma*; Peter van Beek Accurate Multi-Endpoint Molecular Toxicity Predictions in Humans with Contrastive Explanations Bhanushee Sharma*; Vijil Chenthamarakshan; Amit Dhurandhar; Shiranee Pereira; James Hendler; Jonathan S Dordick; Payel Das Visual Question Answering (VQA) Models for Hypothetical Reasoning Shailaja K Sampat* Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment Hanxiao Chen* Importance of Data Re-Sampling and Dimensionality Reduction in Predicting Students’ Success Eluwumi Folake Buraimoh*; Ritesh Ajoodha; Kershree Padayachee Using computer vision to measure spatial-temporal change of building conditions in neighborhoods with street view imagery Evelyn C Fitzgerald*; Tingyan Deng; Daniel Chen; Lijing Wang; Jackelyn Hwang Efficient evaluation metrics for evaluating the performance of GANs Architecture Ramat Ayobami Salami*; Sakinat O Folorunso Targeted active semi supervised learning for new customers in virtual assistants Dieu Thu Le; Verena Weber*; Melanie Bradford Graph Neural Networks for automated histologic scoring of NASH liver biopsy Maryam Pouryahya*; Jason Wang; Kenneth Leidal; Harsha Pokkalla; Dinkar Juyal; Zahil Shanis; Aryan Pedawia; Quang Huy Le; Victoria Mountain; Sara Hoffman; Murray Resnick; Michael Montalto; Andy Beck; Katy Wack; Ilan Wapinski; Oscar Carrasco-Zevallos; Amaro Taylor-Weiner Application of a Bayesian CAR Prior to Analyzing Ancient Statistical Records of the Inca Empire Anastasiya Travina* Depth without the Magic: Inductive Biases of Natural Gradient Descent Anna Mészáros*; Anna Kerekes; Ferenc Huszar We have three types of mentorship roundtables: Research roundtables, Career and Life roundtables, and Sponsor roundtables. The mentorship session is at two time slots on Fri Dec 10, 11:00 AM - 12:45 PM and 20:15 PM - 21:55 PM (UTC time in 24-hour format) in the Roundtable Rooms in WiML Gather.Town. To allow WiML attendees to gain exposure to a wide range of topics, as well as to allow mentors and mentees to connect with a variety of people, attendees (but not mentors) at the mentorship session will rotate between the discussion tables throughout the event. Specifically, there will be 3 timed opportunities to rotate between the Research, Career and Life, and Sponsorship tables. Each discussion period will last for approximately 15 minutes (during which the participants will be asked to remain at their seats for the ongoing conversation). After each 15-minute session, the WiML organizers will announce that it is time for the participants to move to a different roundtable, and all participants will have 10 minutes to explore the different rooms and decide on their discussion topic of interest. All mentors and mentees will be free to use the last 10 minutes of the roundtables session as they wish, either remaining at their discussion tables or moving between tables to meet the other WiML participants. Mentorship Roundtable I: Friday, December 10: 11:00 - 12:45 (UTC time in 24-hour format) Table 1 (Research): Reinforcement learning I (Feryal Behbahani) Table 2 (Research): Reinforcement learning II (Minhae Kwon) Table 3 (Research): Control and online learning (Katja Hofmann) Table 4 (Research): Probabilistic graphical models and Bayesian methods (Isabel Valera) Table 5 (Research): Statistical inference and estimation (Emtiyaz Khan) Table 6 (Research): Learning theory (Arthur Gretton) Table 7 (Research): AutoML (Katharina Eggensperer) Table 8 (Research): Computer Vision (Enzo Ferrante) Table 9 (Research): Robotics (Daniela Pamplona) Table 10 (Research): Fairness, accountability, and ethics in machine learning (Jessica Schrouff) Table 11 (Research): Social science applications (Alice Oh) Table 12 (Career and Life): Navigating the job search (industry) and doing research in the industry (Lavanya Tekumalla) Table 13 (Career and Life): Developing a long-term research plan (Ferenc Huszar) Table 14 (Career and Life): Navigating academia (job search and tenure application process) (Razvan Pascanu) Table 15 (Career and Life): Choosing between academia and industry (Alessandra Tosi) Table 16 (Career and Life): Seeking funding (academia edition): PhD fellowships / professorship grants (Ioana Bica) Table 17 (Career and Life): Establishing collaborations (Yarin Gal) Table 18 (Career and Life): Work-life balance (academia) (Nando de Freitas) Table 19 (Career and Life): Surviving graduate school (Truyen Tran) Table 20 (Career and Life): Work-life balance (industry) (Shane Legg) Table 21 (Career and Life): Scientific communication (Shakir Mohamed) Table 22 (Career and Life): Taking on the leadership roles (academic + industry) (Po-Ling Loh) Table A (Sponsor): Entering AI Research from other STEM fields [Deepmind] (Andy Brock, Michela Paganini) Table B (Sponsor): Careers at G-Research [G-Research] (Clara Dolfen, Jamie Watson, Olivia Bateman) Table C (Sponsor): Machine Learning at Microsoft Research [Microsoft] (Rianne van den Berg) Table D (Sponsor): Careers at NVIDIA [NVIDIA] (Monica Spehar) Mentorship Roundtable II: Friday, December 10: 20:15 - 21:55 PM (UTC time in 24-hour format) Table 1 (Research): Deep learning (Ian Goodfellow) Table 2 (Research): Reinforcement learning I (Sham Kakade) [Canceled] Table 3 (Research): Reinforcement learning II (Amy Zhang) Table 4 (Research): Optimization (Tatjana Chavdarova) Table 5 (Research): Learning theory (Karan Singh) Table 6 (Research): Natural language processing (Layla El Asri) Table 7 (Research): Data-efficient machine learning (Nicolas Le Roux) Table 8 (Research): Interpretability and explainability in machine learning (Jennifer Wortman Vaughan) Table 9 (Research): Causal inference and counterfactuals (Sarah Tan) Table 10 (Research): Robotics (Eugene Vinitsky) Table 11 (Research): Computer vision (Jennifer Hobbs) Table 12 (Research): Music applications (Pablo Samuel Castro) Table 13 (Research): Machine learning for healthcare (Adriana Romero-Soriano) Table 14 (Research): AI 4 Science (Animashree Anandkumar) Table 15 (Career and Life): Navigating the job search (industry) and doing research in industry (Timnit Gebru) Table 16 (Career and Life): Finding mentors throughout your career (Yisong Yue) Table 17 (Career and Life): Navigating academia (job search and tenure application process) (Sinead Williamson) Table 18 (Career and Life): Choosing between academia and industry (Negar Rostamzadeh) Table 19 (Career and Life): Seeking funding: negotiating compensation in industry (Samy Bengio) Table 20 (Career and Life): Establishing collaborations (Anitha Vijayakumar) Table 21 (Career and Life): Surviving graduate school (Katherine Niehaus) Table 22 (Career and Life): Building your professional brand (Chelsea Finn) Table 23 (Career and Life): Work-life balance (industry) (Wonmin Byeon) Table 24 (Career and Life): Life with kids (Sarah Poole) Table 25 (Career and Life): Scientific communication (Been Kim) Table 26 (Career and Life): Non-traditional paths to machine learning (Jennifer Wei) Table 27 (Career and Life): Doing a postdoc I (Hyeji Kim) Table 28 (Career and Life): Networking (Bethany Edmunds) Table 29 (Career and Life): Democratizing ML research: Non-traditional research methods (Jade Abbott) Table E (Sponsor): Apple Internships [Apple] (Lauren Araujo, Lauren Hannah) Table F (Sponsor): Task Oriented Dialog Research @ ASAPP [ASAPP] (Ramya Ramakrishnan, Ryan McDonald, Sravana Reddy) Table G (Sponsor): Careers in AI and ML at Capital One [Capital One] (Helen Lee-Righter, Vannia Gonzalez Macias) Table H (Sponsor): Machine Learning at D. E. Shaw Research [D. E. Shaw Research] (Jocelyn Sunseri) Table I (Sponsor): Present and Future of AI Research at Intel [Intel] (Huma Abidi, Lama Nachman) Table J (Sponsor): AI Careers at Meta (Beliz Gokkaya, Kavya Srinet, Sahar Karimi) Table K (Sponsor): Machine Learning at Microsoft Research [Microsoft] (John Langford, Nicolas Le Roux) Table L (Sponsor): From Academia to Quantitative Finance – Careers at PDT Partners [PDT Partners] (Kurt Miller, Winnie Yang) Table M (Sponsor): Meet & Greet Qualcomm AI Research [Qualcomm] (Dipika Khullar, Sangeetha Siddegowda, Shreya Kadambi) Table N (Sponsor): QuantumBlack Careers Roundtable [QuantumBlack] (Huilin Zeng, Marta Lopez, Xilin Cecilia Shi) Table O (Sponsor): Early Career Advice for Industry [Salesforce] (Shelby Heinecke, Vena Li) Table P (Sponsor): Software Story for Accelerators and Engineer Experience in a Startup World [SambaNova Systems] (Mary Jo Doherty, Weiwei Chen) Table Q (Sponsor): Self-Driven Women: Careers at Waymo [Waymo] (Drago Anguelov, Wei Chai, Chen Wu, Congcong Li, Kevin Peterson) There will also be sponsor booths in the expo room, staffed at the times below. Sponsor talks are playable by participants on-demand in the Sponsor Expo Room at Gather.Town and at the NeurIPS virtual site (NeurIPS registration required to access). Virtual Booths (in the Sponsor Expo Room at Gather.Town ) Apple (Fri Dec 10, 20:15 - 21:55 UTC) Apple at NeurIPS | Career Opportunities | Internship Opportunities | RSVP to Meet Apple | RSVP to Internship Q&A Panel Capital One (Fri Dec 10, 20:15 - 21:55 UTC) Learn more about AI and ML at Capital One | Explore Data Science Roles at Capital One DeepMind (Fri Dec 10, 11:00 - 13:45 UTC) DeepMind at NeurIPS 2021 - Schedule | DeepMind - Careers Info D. E. Shaw Research (Fri Dec 10, 13:00 - 13:45 UTC & 20:15 - 21:55 UTC) D. E. Shaw Research - Brochure G-Research (Fri Dec 10, 12:45 - 13:45 UTC) G-Research: Opportunities | G-Research: Kaggle Competition | G-Research: Spring Insight Week Meta (Fri Dec 10, 20:15 - 21:55 UTC) Meta AI at NeurIPS 2021 | Meta AI Careers Microsoft (Fri Dec 10, 20:15 - 21:55 UTC) Microsoft Research – Emerging Technology, Computer, and Software Research | Microsoft at NeurIPS 2021 - Microsoft Research NVIDIA (Fri Dec 10, 11:00 - 13:45 UTC & 20:15 - 21:55 UTC) NVIDIA @ NeurIPS2021 | NVIDIA Careers | Research at NVIDIA QuantumBlack (Fri Dec 10, 20:15 - 21:55 UTC) SambaNova Systems (Fri Dec 10, 20:15 - 21:55 UTC) ML Accelerators and Performance Sponsor Talks (in the Sponsor Expo Room at Gather.Town and at the NeurIPS virtual site ) Lizi Ottens (Apple) Machine Learning at Apple Cat Posey (Capital One) AI & ML at Capital One Jocelyn Sunseri (D. E. Shaw Research) Machine Learning Initiatives at D. E. Shaw Research Mihaela Rosca, Feryal Behbahani, and Kate Parkyn (DeepMind) Women at DeepMind - Applying for Technical Roles Daniela Massiceti (Microsoft) Advancing real-world few-shot learning with the new ORBIT dataset Anima Anandkumar (NVIDIA) Research at NVIDIA: New Core AI and Machine Learning Lab Garazi Gomez-de-Segura (QuantumBlack) ML for Engineering design Anna Bethke (Salesforce) Actionable Steps to Implement Ethics by Design Qinghua Li (SambaNova) SambaNova Systems: ML Accelerators & Performance Chen Wu (Waymo) Machine Learning for Autonomous Driving at Waymo The following participant-hosted socials will take place before, during, and after the workshop in Gather.Town in the South Garden in WiML Gather.Town. We highly encourage WiML participants to attend, to meet fellow participants in a fun and casual setting! See a description of each social here , and instructions on how to enter the area for each social here ! Pre-workshop socials Dec 9, 2:00 - 3:00 Indrapriyadarsini Sendilkkumaar, Shahrzad Mahboubi – Optimization Techniques Ayushi Sharma, Kiana Alikhademi – Applying to and Navigating PhDs Workshop socials Dec 10, 3:00 - 4:00 Jenna Hong, Devi Bhattarai – Multidisciplinary AI (Neuroscience, Social Science and Ethics) Hope Schroeder, Akshita Ramya Kamsali – Natural Language Processing and Computational Social Science Bing Zhang -- Win and Wine (Fun Social) Dec 10, 5:30 - 7:00 Mehreen Ali, Anoush Najarian – Privacy and Algorithms of Oppression Melissa Fabros -- Murder Mystery (Fun Social) Dec 10, 6:00 - 7:00 Mamatha Thota, Naina Dhingra – Computer Vision Algorithms and Applications Call for Participation WiML Workshop 2021 @ NeurIPS 16th Workshop for Women in Machine Learning Submissions are now closed, but if you would like to participate as a volunteer, poster mentor, or social host, please apply here before November 5, 2021 to be considered! The 16th Workshop for Women in Machine Learning (WiML) will be co-located with NeurIPS in December 2021 and will be held virtually. The workshop is a one-day event with invited speakers, oral and poster presentations. The event brings together members of the academic and industry research community for an opportunity to connect, exchange ideas, and learn from each other. Underrepresented groups and undergraduates interested in pursuing machine learning research are encouraged to participate. There will be virtual mentorship sessions to discuss current research trends and career choices in machine learning. While all presenters will identify as a woman, nonbinary or gender non-conforming, members of all gender identities are invited to attend. All submissions must abide by the WiML Code of Conduct . Submission page: https://cmt3.research.microsoft.com/WiML2021 Registration funding and non-author participation application: here . IMPORTANT DATES September 1, 2021 - Abstract submission opens on CMT October 5, 11:59 pm AoE - Abstract submission deadline October 20, 2021 - Notification of abstract acceptance October 20, 2021 - Application for registration fee funding and volunteering opens November 5, 2021 - Registration funding application deadline November 12, 2021 - Registration funding notification December 9-10, 2021 - WiML Workshop Day SUBMISSION INSTRUCTIONS We strongly encourage students, postdocs, and researchers in all areas of machine learning who identify as a woman, nonbinary or gender non-conforming to submit an abstract (1 page PDF) describing new, previously, or concurrently published research. We welcome abstract submissions in theory, methodology, as well as applications. While the presenting author need not be the first author of the work, we request that the presenting author be identifying as a woman, nonbinary or gender non-conforming. Submissions will be reviewed in a double-blind setting. Authors of accepted abstracts will be asked to present their work in a virtual poster session. A few authors will be selected to give spotlight or oral presentations. There are no formal proceedings. Abstracts are non-archival: they may describe completed research or work-in-progress. Please refer to the detailed Submission Instructions . REGISTRATION FEE FUNDING Registration to the NeurIPS virtual conference is required to participate in this year's WiML workshop. Registration fee funding for NeurIPS will be available for eligible WiML participants. To qualify, the participant must be a student, postdoc, or equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented regions or groups), and identify as a woman, nonbinary or gender non-conforming. Priority will be given to poster presenters, workshop volunteers, and first-time attendees of NeurIPS or similar conferences. Funding recipients must participate in the WiML Workshop as either a poster presenter or volunteer as outlined in the application. Funding and volunteering application form: Please check starting October 20, 2021 for the application link, when it will be made ready. The application deadline is November 5, 2021. VOLUNTEERING We are seeking volunteers to help with technical setup and virtual technology testing before and during the event, e.g., letting people into Zoom rooms, poster mentors etc. You can indicate if you can help in any way in the application form. OTHER SUBSIDIES We will also consider internet and equipment subsidies for the purpose of attending the workshop. Equipment may include headphones, microphones, funding to cover internet access, and anything else that might facilitate participation in the workshop. Please see the funding and volunteering application form for details. Questions? Check out the FAQs (https://wimlworkshop.org/faq/ ) or reach us at workshop@wimlworkshop.org PLATINUM SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS Committee ORGANIZERS Boyi Li General Chair Mariya I. Vasileva General Chair Linh Tran Finance and Sponsorship Chair Akiko Eriguchi Finance and Sponsorship Chair Meera Desai Logistic Chair S. Aga Lee Logistic Chair Jieyu Zhao Senior Program and Mentorship Chair Salomey Osei Senior Program and Mentorship Chair Sirisha Rambhatla Senior Program and Mentorship Chair Geeticka Chauhan Student Program and Funding Chair Nwamaka Okafor Student Program and Funding Chair ADVISORY Danielle Belgrave D&I chair Nezihe Merve Gürel WiML Board POC SUPER VOLOUNTEERS Mennatullah Siam University of Alberta Tianlin Xu London School of Economics Weiwei Zong Henry Ford Health System & University of Michigan Gloria Namanya Makerere University Sharvaree Vadgama University of Amsterdam Archana Iyer Sloan Kettering Institute Sofia Bourhim ENSIAS-Mohammed V University Silvia Pagliarini University of California, Los Angeles Liyue Shen Stanford University Maikey Khorani Salahaddin University / College of Engineering Disha Shur Purdue University Naiti Bhatt New York University Patricia Robinson Stanford University Sandareka Wickramanayake National University of Singapore Priya Bannur University of Southern California Varsha Kishore Cornell University Ria Vinod Brown University, IBM Research Niharika Vadlamudi International Institute of Information Technology, Hyderabad Bing Zhang IBM Research Mei Chen University of Waterloo Kajal Puri University of Bonn, Germany AREA CHAIRS Deepti Ghadiyaram Facebook Research Adriana Romero Facebook AI Research Amita Misra IBM Anastasiya Belyaeva MIT Angelica Aviles-Rivero University of Cambridge Ankita Shukla ASU Anna Klimovskaia Susmelj Swiss Data Science Center Anna Kruspe Technische Universität München Besmira Nushi Microsoft Research Buket Yüksel Koç University Celestine Mendler-Dünner UC Berkeley Dalin Guo UC San Diego; Twitter Inc. Erin Grant UC Berkeley Gintare Karolina Dziugaite ServiceNow Ilke Demir Intel Corporation Isabela Albuquerque Institut National de la Recherche Scientifique Kalesha Bullard Facebook AI Research Kuan-Ting Chen National Taiwan University Maria Glenski Pacific Northwest National Laboratory Mayoore Jaiswal University of Washington Mengjiao Wang Amazon Visual Search Nastraran Baradaran Citrix Systems Natalia Efremova Queen Mary University London Nesime Tatbul Intel Labs and MIT Nezihe Merve Gürel ETH Zürich Niha Beig Case Western Reserve University Nora Hollenstein University of Copenhagen Obioma Pelka University of Applied Sciences and Arts Dortmund Pallika Kanani Oracle Labs Peixian Liang University of Notre Dame Pooja Sharma BIT Sindri Rachel Cummings Georgia Tech Samira Daruki Expedia Research Sandhya Prabhakaran Moffitt Cancer Center Sandya Mannarswamy Intel India Sara Magliacane IBM Research Sergul Aydore Amazon Web Services Shinjini Ghosh MIT Shuai Zhang ETH Zürich Sima Behpour Samsung Research America Sinead Williamson UT Austin Spandana Gella Amazon AI Subarna Tripathi Intel Labs Surangika Ranathunga University of Moratuwa Swetasudha Panda Oracle Labs Tania Lorido-Botran Independent Researcher Xenia Miscouridou Imperial College London Xi Rao ETH Zürich Xiao Zhang T-Mobile Xun Tang Yelp Yao Qin University of California, San Diego FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here! I am a man. Can I attend WiML? Yes. Allies are welcome to attend! Note, however, that all speakers and poster presenters will primarily identify as women, nonbinary, or gender-nonconforming, as our goal is to promote them and their work within the machine learning community. What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

< Back WiML Breakfast @ ICML 2015 Lille, France WiML is hosting a breakfast at ICML 2015 in Lille, France. Previous Next

< Back 4th WiML Mentorship Program for PhD Applications: Panel on Funding and Work-Life Balance Virtual 4th WiML Mentorship Program 2024-2025 Previous Next

WiML is dedicated to providing an experience for all participants that is free from harassment, bullying, discrimination, and retaliation. WiML Code of Conduct The open exchange of ideas, the freedom of thought and expression, and respectful scientific debate are central to the goals of Women in Machine Learning, Inc. (“WiML”) activities; this requires a community and an environment that recognizes and respects the inherent worth of every person. The purpose of this Code of Conduct (CoC) is to outline expected standards of behaviour during WiML activities. Scope This CoC applies to all WiML activities, including but not limited to: Events organized, hosted, co-branded, or in cooperation with WiML Submissions and reviewing processes run by WiML. Communications sent through communication channels associated with WiML, including but not limited to social media. Meetings and discussions associated with WiML activities. If an activity is in cooperation with another organization, if the other organization has its own CoC, the union of both CoCs apply. Responsibility All attendees, speakers, mentors, panelists, area chairs, reviewers, sponsors, contractors, organizers, volunteers, members of the WiML Board of Directors and Senior Advisory Council (referred to as “Participants” collectively throughout this document) involved in WiML activities as described above are required to comply with this CoC. Reviews should actively avoid subtle discrimination, however inadvertent. In particular, reviewers should avoid comments in reviews about English style or grammar that may be interpreted as implying that the author is “foreign” or “non-native”. Sponsors are equally subject to this CoC. In particular, sponsors should not use images, activities, or other materials that reinforce gender stereotypes or are of a sexual, racial, or otherwise offensive nature at WiML events. Booth staff, including but not limited to volunteers, should not create a sexualized environment. Unacceptable Behavior WiML is dedicated to providing an experience for all participants that is free from harassment, bullying, discrimination, and retaliation. This includes offensive comments related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), politics, technology choices, or any other personal characteristics or considerations made unlawful by federal, state, or local laws, ordinances, or regulations. Inappropriate or unprofessional behavior that interferes with another participant’s full participation will not be tolerated. This includes bullying, intimidation, personal attacks, harassment, sustained disruption of talks or other events, sexual harassment, stalking, following, harassing photography or recording, inappropriate physical contact, unwelcome sexual attention, public vulgar exchanges, derogatory name-calling, or diminutive characterizations, all of which are unwelcome in this community. Advocating for, or encouraging, any of the above behaviour, is also considered harassment. No use of images, activities, or other materials that are of a sexual, racial, or otherwise offensive nature that may create an inappropriate or toxic environment is permitted. Disorderly, boisterous, or disruptive conduct including but not limited to fighting, coercion, theft, damage to property, or any mistreatment or non-businesslike behavior towards other participants is not tolerated. Scientific misconduct—including but not limited to fabrication, falsification, or plagiarism of paper submissions or research presentations—is prohibited. Reporting If you have concerns related to your participation or interaction at a WiML activity, observe someone else’s difficulties, or have any other concerns you wish to share, you can make a report: Anytime: By email at codeofconduct@wimlworkshop.org ‬ During an event: In-person to organizers, volunteers, or any member of the WiML Board of Directors. They will then direct you to the designated responder(s) for that event. Organizers and volunteers can be identified by special badges marked as “ORGANIZER” or “VOLUNTEER”. Members of the WiML Board of Directors can be identified by special badges marked as “WiML Board”. There is no deadline by which to make a report. If the person receiving your report is not the designated responder for that event, they will direct you to a designated responder and/or provide you immediate medical or security help and assist you to feel safe for the duration of the activity. Designated responders will follow WiML procedures to respond to and investigate your report. Enforcement Any participant asked by any member of the community to stop any unacceptable behavior is expected to comply immediately. A response of “just joking” will not be accepted; behavior can be harassing without an intent to offend. If a participant engages in behaviour that violates this CoC, WiML retains the right to take any action deemed appropriate, including but not limited to: Formal or informal warnings Barring or limiting continued attendance and participation, including but not limited to expulsion from the event Barring from participating in or deriving benefits from future WiML activities Exclusion from WiML opportunities, e.g. leadership, organizing, volunteering, speaking, reviewing, sponsoring, etc. Reporting the incident to the offender’s local institution or funding agencies Reporting the incident to local law enforcement The same actions may be taken toward any individual who engages in retaliation or who knowingly makes a false allegation of harassment. If action is taken, an appeals process will be made available. Investigation Reports of violations will be handled at the discretion of the WiML Board of Directors, who will investigate reports and bring the issue to resolution. Reports made during the activity will be responded to within 24 hours; reports made at other times will be responded to in less than five weeks. All reports will be handled as confidentially as possible and information will be disclosed only as it is necessary to complete the investigation and bring to resolution. There may be situations (such as those involving Title IX issues in the United States and venue- or employer-specific policies) where the member of the WiML Board of Directors informed of the violation will be under an obligation to file a report with another individual or organization outside of WiML. Ongoing Review The WiML Board of Directors welcomes feedback from the community on this CoC policy and procedures; please contact us by email at info@wimlworkshop.org . Acknowledgements This CoC policy was written by adapting the wording and structure from other CoC policies and procedures by Geek Feminism Wiki (created by the Ada Initiative), NeurIPS , ACM , Montreal AI Symposium , and Deep Learning Indaba .

< Back WiML Workshop 2012 Lake Tahoe, Nevada The 7th annual Women in Machine Learning workshop was colocated with NIPS 2012 in Lake Tahoe, Nevada in December 2012. Previous Next

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 17th Women in Machine Learning Workshop (WiML 2022) The Workshop is co-located with NeurIPS on Monday, November 28th, 2022 at the New Orleans Convention Center in Louisiana, USA. Speakers Logistics Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications, it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning. This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. Now in its 17th year, the 2022 workshop is co-located in-person with NeurIPS 2022 at New Orleans in Louisiana. Besides this annual workshop, Women in Machine Learning also organizes events such as lunch at ICML and AAAI conferences, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Alice Oh KAIST University Hima Lakkaraju Harvard University Bianca Zadrozny IBM Research Raesetje Sefala Distributed AI Research Institute Location The in-person workshop will be co-located with NeurIPS in New Orleans, Louisiana on Monday, November 28th, 2022 . Poster Dimensions In person event: Please pay attention to the dimensions of your posters. Workshop posters should be printed on thin paper, not laminated and no larger than 24 inches wide x 36 inches high. See more info regarding workshop poster dimensions here . Virtual event: (details coming soon) Childcare NeurIPS is kindly providing free onsite childcare to participants this year. Registration to the NeurIPS conference is required to participate in this year's WiML workshop. For more information on how to register for the childcare service, please visit the NeurIPS website. Information for authors Authors may find information about poster preparation and upload here . We are looking forward to welcoming you! Head over to our program webpage to check the list of speakers! In order to attend our event, you complete both steps below: Register to NeurIPS 2022 here . To attend our in-person event, you need to register either for the NeurIPS “Conference” or Workshops” session. Registering for either physical component will grant you access to WiML . If you are attending virtually, the NeurIPS “Virtual Only Pass” will suffice. Early registration (with reduced rates) ends on October the 14th. Information about visa application, including visa letter invitation can be found here . Fill in our WiML registration form (free) here . This form helps us keep a headcount. Authors, co-authors and volunteers will be receiving promo codes by email. Authors and co-authors of accepted abstracts, area chairs and volunteers, should have already received promo-codes they may add during the Eventbrite registration. Note: Any physical (in-person) registration includes the NeurIPS ‘Virtual Only Pass’ and as such provides a pass to our virtual component too. Attendees that are attending only virtually using the NeurIPS “Virtual Only Pass” registration, they will be able to access the livestream links of our in-person event. PROGRAM PANELISTS MENTORSHIP ROUNDTABLES LIST OF ACCEPTED POSTERS LIST OF REVIEWERS Monday, November 28, 2022 [in-person] (Time in CT) Morning Session 7:30 am - 8:30 am Registration & Breakfast 8:30 am - 8:45 am Opening Remarks - Konstantina Palla (Senior Program Chair) 8:45 am - 9:00 am D&I Chair remarks - Danielle Belgrave 9:00 am - 9:10 am Contributed talk (Tejaswi Kasarla ) - "Maximum Class Separation as Inductive Bias in One Matrix" 9:10 am - 9:20 am Contributed talk (Taiwo Kolajo ) - "Pre-processing of Social Media Feeds based on Integrated Local Knowledge Base" 9:20 am - 9:55 am Invited talk - Alice Oh - "The importance of multiple languages and multiple cultures in NLP research" 9:55 am - 10:10 am Coffee break 10:10 am - 10:25 am WiML Board Remarks - Jessica Schrouff 10:25 am - 11:00 am Invited talk - Raesetje Sefala - "Constructing visual datasets to answer research questions" 11:00 am - 11:10 am Contributed talk (Pascale Gourdeau ) - "When are Local Queries Useful for Robust Learning?" 11:10 am - 11:20 am Contributed talk (Annie S Chen ) - "You Only Live Once: Single-Life Reinforcement Learning" 11:20 am - 1:20 pm Mentorship roundtables & Lunch - Mentors: Adam Roberts, Stephanie Hyland, Bianca Zadrozny, Sima Behpour, Mercy Asiedu, Franziska Boenisch, Eleni Triantafillou, Isabela Albuquerque, Yisong Yue, Amy Zhang, Zelda Mariet, Tristan Naumann, Danielle Belgrave, Shakir Mohamed, Tong Sun, Gintare Karolina Dziugaite, Samy Bengio, Rianne van den Berg, Maja Rudolph, Luisa Cutillo, Ioana Bica, Clara Hu, Rosanne Liu, Jennifer Wei, Alice Oh, SueYeon Chung, Erin Grant, Sasha Luccioni, Michela Paganini, Mounia Lalmas-Roelke, Claire Vernade, Alekh Agarwal, Neema Mduma, Vinod Prabhakaran, Savannah Thais, Jonathan Frankle, Ce Zhang, Rose Yu, Jessica Schrouff, Bo Li, Katherine Heller, Ben Poole, Setareh Ariafar, Christina Pavlopoulou, Isabel Morlidge, Kavya Srinet, Cheng Zhang, Elise van der Pol, Diana Montanes, Lise Diagne, Le Yu, Megan Forrester. Afternoon Session 1:20 pm - 1:55 pm Invited talk - Bianca Zadrozny - "Machine Learning for Climate Risk" 1:55 pm - 2:05 pm Contributed talk (Elizabeth Bondi-Kelly) - "Human-AI Interaction in Selective Prediction Systems" 2:05 pm - 2:15 pm Contributed talk (Gowthami Somepalli) - "Investigating Reproducibility from the Decision Boundary Perspective." 2:15 pm - 2:35 pm Coffee break 2:35 pm - 3:10 pm Invited talk -Hima Lakkaraju - "A Brief History of Explainable AI: From Simple Rules to Large Pretrained Models" 3:10 pm - 4:10 pm Panel discussi on 4:10 pm - 4:20 pm Closing Remarks 4:20 pm - 4:30 pm Poster setup 4:30 pm - 6:00 pm Joint Affinity Groups Poster Session Monday, December 5, 2022 [virtual] (Time in ET) 9:30 am - 9:40 am Opening Remarks 9:40 am - 9:55 am Contributed talk (Okechinyere J Achilonu ) - "Natural language processing for automated information extraction of cancer parameters from free-text pathology reports" 9:55 am - 10:10 am Contributed talk (Paula Harder ) - "Physics-Constrained Deep Learning for Climate Downscaling" 10:10 am - 10:25 am Contributed talk (Silvia Tulli ) - "Explanation-Guided Learning for Human-AI collaboration" 10:25 am - 10:40 am Contributed talk (Mina Ghadimi Atigh ) - "Hyperbolic Image Segmentation" 10:40 am - 10:50 am Set up (for mentorship session) 10:50 am - 11:50 am Mentorship Panel (Discussion + Q&A) with Jenn Wortman Vaughan (Microsoft Research), Colin Raffel (University of North Carolina) Kristen Grauman (University of Texas at Austin) 11:50 am - 12:00 pm Break 12:00 pm - 12:35 pm Sponsor Talks 2:00 pm - 4:00 pm Joint Affinity Groups Poster Session Alice Oh KAIST University Hima Lakkaraju Harvard University Bianca Zadrozny IBM Research Raesetje Sefala Distributed AI Research Institute Rianne van den Berg Microsoft Research AI and Creativity: Adam Roberts (Google Brain) Choosing between Academia and Industry: Stephanie Hyland (Microsoft Research) and Bianca Zadrozny (IBM Research) Continual Learning & Open-World Learning: Sima Behpour (Bosch) Founding and Funding Startups: Mercy Asiedu (Google) Gender-related challenges: Franziska Boenisch (Vector Institute) Generalization & Robustness: Eleni Triantafillou (Google Brain) and Isabela Albuquerque (DeepMind) Getting a job (academia): Yisong Yue (Caltech) and Amy Zhang (UT Austin) Getting a job (industry): Zelda Mariet (Google) Healthcare/clinical applications: Danielle Belgrave (DeepMind) and Tristan Naumann (Microsoft Research) Leadership: Shakir Mohamed (DeepMind) and Tong Sun (Adobe) Learning theory: Karolina Dziguaite (Google Brain) Life in industry research: Samy Bengio (Apple) and Rianne van den Berg (Microsoft Research) Life with kids: Maja Rudolph (BCAI) and Luisa Cutillo (University of Leeds) Mental health & surviving in grad school: Ioana Bica (DeepMind), Clara Hu (Google Brain), and Rosanne Liu (Google Brain) ML for Science: Jennifer Wei (Google) Natural language processing: Alice Oh (KAIST) Negotations in ML: Nicole Bannon (81cents) Neuroscience & cognitive science: Erin Grant (UCL), SueYeon Chung (NYU/Flatiron Institute), and Noga Zaslavsky Non-traditional paths in machine learning: Sasha Luccioni (HuggingFace) and Michela Paganini (DeepMind) Recommender systems: Mounia Lalmas-Roelke (Spotify) Reinforcement learning: Claire Vernade (DeepMind), Alekh Agarwal (Google), and Elise van der Pol (Microsoft Research) Seeking funding in academia: Neema Mduma (The Nelson Mandela African Institution of Science and Technology) Social science applications: Vinod Prabhakaran (Google Research), Savannah Thais (Columbia University), and Sarah Brown (University of Rhode Island) Systems and machine learning: Jonathan Frankle (Harvard University/MosaicML) and Ce Zhang (ETH Zurich) Time Series: Rose Yu (UCSD) Trustworthy machine learning: Jessica Schrouff (DeepMind), Bo Li (UIUC), and Katherine Heller (Google Research) ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier That Validates 301 New Exoplanets Noa Lubin (Bar-Ilan University)* When are Local Queries Useful for Robust Learning? Pascale Gourdeau (University of Oxford)*; Varun Kanade (University of Oxford); Marta Kwiatkowska (Oxford University); James Worrell (University of Oxford) Natural language processing for automated information extraction of cancer parameters from free-text pathology reports Okechinyere J Achilonu (University of the Witwatersrand)* Discriminative Candidate Selection for Image Inpainting Lucia Cipolina-Kun (University of Bristol)*; Simone Caenazzo (Riskcare); Sergio Manuel S Papadakis (Konfío) Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel Abigail O Jefia (Cisco Systems)*; Segun Popoola (Manchester Metropolitan University); Aderemi A. Atayero (Covenant University) Development of predictive model for survival of paediatric HIV/AIDS patients in south western Nigeria using data mining techniques Agbelusi Olutola (Rufus Giwa Polytechnic )* Modelling non-reinforced preferences using selective attention Noor Sajid (University College London)*; Panagiotis Tigas (Oxford University); Zafeirios Fountas (Huawei Technologies); Qinghai Guo (Huawei Technologies); Alexey Zakharov (Huawei Technologies); Lancelot Da Costa (Imperial College London) Data Analysis and Machine Learning for Speech Music Playlist Generation Maikey Zaki Khorani (Salahaddin University/College of Engineering)* Meta Optimal Transport Brandon Amos (Facebook AI Research); Samuel Cohen (University College London); Giulia Luise (University College London)*; Ievgen Redko (Aalto University) Single-modality and joint fusion deep learning for diabetic retinopathy diagnosis Sara EL-ATEIF (ENSIAS)*; Ali Idri (University Mohamed V) Multi-Armed Bandit Problem with Temporally-Partitioned Rewards Giulia Romano (Politecnico di Milano)*; Andrea Agostini (Politecnico di Milano); Francesco Trovò (Politecnico di Milano); Nicola Gatti (Politecnico di Milano); Marcello Restelli (Politecnico di Milano) The use of Region-based Convolutional Neural Network Model for Analysing Unmanned Aerial Vehicle Remote Sensing Odunayo E Oduntan (Chrisland University Abeokuta, Nigeria)* Kernel Density Bayesian Inverse Reinforcement Learning Aishwarya Mandyam (Stanford University)*; Didong Li (University of North Carolina); Diana Cai (Princeton University); Andrew Jones (Princeton University Department of Computer Science); Barbara Engelhardt (Stanford University) Multimodal Checklists for Fair Clinical Decision Support Qixuan Jin (Massachusetts Institute of Technology)*; Marzyeh Ghassemi (University of Toronto) Investigating Reproducibility from the Decision Boundary Perspective Gowthami Somepalli (University of Maryland, College Park)*; Arpit Bansal (University of Maryland - College Park); Liam Fowl (University of Maryland); Ping-yeh Chiang (University of Maryland, College Park); Yehuda Dar (Rice University); Richard Baraniuk (Rice University); Micah Goldblum (University of Maryland); Tom Goldstein (University of Maryland, College Park) Development of a modified likelihood ratio model for handwriting identification in forensic science Adeyinka O Abiodun (National Open University of Nigeria)*; Sesan Adeyemo (University of Ibadan); Adegboyega Adebayo (National Open University of Nigeria) Self-Supervised Graph Representation Learning for chip design-partitioning on multi-FPGA platforms Divyasree Tummalapalli (Intel Corporation)*; Chiranjeevi Kunapareddy (Intel Corporation); Vikas Akalwadi (Intel Corporation); Rahul Govindan (Intel Corporation); Balaji G (Intel Corporation) Hierarchically Clustered PCA and CCA via a Convex Clustering Penalty Amanda M Buch (Weill Cornell Medicine, Cornell University)*; Conor Liston (Weill Cornell Medicine, Cornell University); Logan Grosenick (Weill Cornell Medicine, Cornell University) Deep Metric Learning to predict cardiac pressure with ECG Hyewon Jeong (MIT)*; Marzyeh Ghassemi (University of Toronto, Vector Institute); Collin Stultz (MIT) Spatial clustering with random partitions on ovarian cancer data Yunshan Duan (University of Texas at Austin)*; Peter Mueller (University of Texas); Wenyi Wang (MD Anderson); Shuai Guo (MD Anderson) Object Segmentation of Cluttered Airborne LiDAR Point Clouds Mariona Carós (Universitat de Barcelona)*; Ariadna Just (Institut Cartogràfic i Geològic de Catalunya); Santi Seguí (Universitat de Barcelona); Jordi Vitria (Universitat de Barcelona) Identifying Disparities in Sepsis Treatment using Inverse Reinforcement Learning Hyewon Jeong (MIT)*; Taylor W Killian (University of Toronto, Vector Institute); Sanjat Kanjilal (Harvard Medical School); Siddharth Nagar Nayak (Massachusetts Institute of Technology); Marzyeh Ghassemi (University of Toronto, Vector Institute) Multi Mix Mask – RCNN (M3RCNN) for Instance Intervertebral Disc Segmentation Malinda Vania (Ulsan National Institute of Science and Technology)*; Lim Sunghoon (Ulsan National Institute of Science and Technology) The Lean Data Scientist: Recent Advances towards Overcoming the Data Bottleneck Chen Shani (The Hebrew university of Jerusalem)*; Jonathan Zarecki (Bar-Ilan University); Dafna Shahaf (The Hebrew University of Jerusalem) Towards an automatic classification for software requirements written in Spanish María Isabel Limaylla Lunarejo (Universidade da Coruña)* Exploiting Pretrained Biochemical Language Models for Targeted Drug Design Gökçe Uludoğan (Bogazici University)*; Arzucan Özgür (Bogazici University); Elif Ozkirimli (Roche AG); Kutlu Ülgen (Bogazici University ); Nilgün Karalı (Istanbul University) Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty Amelia Jiménez-Sánchez (IT University of Copenhagen)*; Diana Mateus (Centrale Nantes); Sonja Kirchhoff (TUM school of medicine); Chlodwig Kirchhoff (TUM school of medicine); Peter Biberthaler (TUM school of medicine); Nassir Navab ("TU Munich, Germany"); Miguel Angel González Ballester (Universitat Pompeu Fabra); Gemma Piella (Pompeu Fabra University) Shortcuts in Public Medical Image Datasets Amelia Jiménez-Sánchez (IT University of Copenhagen)*; Andreas Skovdal (ITU); Frederik Bechmann Faarup (ITU); Kasper Thorhauge Grønbek (ITU); Veronika Cheplygina (ITU) Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials Alicia Curth (University of Cambridge)*; Alihan Hüyük (University of Cambridge); Mihaela van der Schaar (University of Cambridge) Improving Robustness to Distribution Shift with Methods from Differential Privacy Neha Hulkund (MIT)* Quantifying Gender Bias in Hindi Language Models Neeraja Kirtane (Manipal Institute of Technology)*; V Manushree (Manipal Institute Of Technology); Aditya Kane (Pune Institute of Computer Technology) De novo PROTAC design using graph-based deep generative models Divya V Nori (Massachusetts Institute of Technology)*; Connor Coley (MIT); Rocio Mercado (Massachusetts Institute of Technology) Self-Contained Entity Discovery from Captioned Videos Melika ayoughi (university of amsterdam)*; Paul Groth (University of Amsterdam); Pascal Mettes (University of Amsterdam) Reduce False Negative in Distant supervised learning using Dependency tree-LSTM to Construct a Knowledge Graph Samira Korani (NUIG)*, John McCrae (NUIG) Break the bottleneck of AI deployment at the edge. Paula Ramos (Intel)*; Helena Kloosterman (Intel); Samet Akcay (Intel); Yu-Chun Liu (Intel Corp.); Raymond Lo (Intel) Towards probabilistic end-to-end Deep Learning Weather Forecasting: Spatio-Temporal Temperature Forecasting using Normalizing Flows Christina Winkler (Technical University of Munich)* Bias Assessment of Text-to-Image Models Sasha Luccioni (Mila)*; Clementine Fourrier (Hugging Face); Nathan Lambert (Hugging Face); Unso Eun Seo Jo (Hugging Face); Irene Solaiman (Hugging Face); Helen Ngo (Hugging Face); Nazneen Rajani (Hugging Face); Giada Pistilli (Hugging Face); Yacine Jernite (Hugging Face); Margaret Mitchell (Hugging Face) SO(3) Equivariant Framework for Spatial Networks Sarp Aykent (Auburn University); Tian Xia (Auburn University)* Protein Structure Ranking with Atom-level Geometric Representation Learning Tian Xia (Auburn University)*; Sarp Aykent (Auburn University) Detecting Synthetic Opioids with NQR Spectroscopy and Complex-Valued Signal Denoising Amber J Day (Los Alamos National Laboratory)*; Natalie Klein (Los Alamos National Laboratory); Michael Malone (Los Alamos National Laboratory); Harris Mason (Los Alamos National Laboratory); Sinead A Williamson (UT Austin) Identifying fine climatic parameters for high maize yield using pattern mining: case study from Benin (West Africa) Souand Peace Gloria TAHI (University of Abomey-Calavi)*; Vinasetan Ratheil Houndji (Institut de Formation et de Recherche en Informatique, University of Abomey-Calavi); Castro Hounmenou (Laboratoire de Biomathématiques et d’Estimations Foresti`eres, Faculty of Agronomic Sciences, University of Abomey-Calavi); Romain Glèlè Kakaï (Laboratoire de Biomathématiques et d’Estimations Foresti`eres, Faculty of Agronomic Sciences, University of Abomey-Calavi) GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks Kenza Amara (ETH Zurich)*; Rex Ying (Yale University); Ce Zhang (ETH) Using Visual Similarity to Navigate eCommerce Inventory Shubhangi Tandon (ebay inc)*; Christopher Miller (ebay inc); Selcuk Kopru (ebay); Senthilkumar Gopal (ebay inc) Mixture of Gaussian Processes with Probabilistic Circuits for Multi-Output Regression Mingye Zhu (University of Science and Technology of China)*; Zhongjie Yu (TU Darmstadt); Martin Trapp (Aalto University ); Arseny Skryagin (TU Darmstadt); Kristian Kersting (TU Darmstadt) Synthetic Data Augmentation for Time Series Forecasting Kasumi Ohno (Toyota Technological Institute)*; Kohei Makino (Toyota Technological Institute ); Makoto Miwa (Toyota Technological Institute); Yutaka Sasaki (Toyota Technological Institute) Task-conditioned modelling of drug-target interactions Emma Petersson Svensson (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)*; Pieter-Jan Hoedt (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Guenter Klambauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) Can deep learning models understand natural language descriptions of patient symptoms following cataract surgery? Mohita Chowdhury (Ufonia Limited)*; Oliver Gardiner (Ufonia Limited); Ernest Lim (Ufonia Limited); Aisling Higham (Ufonia Limited); Nick de Pennington (Ufonia Limited) Hyperbolic Image Segmentation Mina Ghadimi Atigh (University of Amsterdam)*; Julian M Schoep (Promaton); Erman Acar (Vrije Universiteit Amsterdam); Nanne van Noord (University of Amsterdam); Pascal Mettes (University of Amsterdam) Maximum Class Separation as Inductive Bias in One Matrix Tejaswi Kasarla (University of Amsterdam)*; Gertjan Burghouts (TNO); Max van Spengler (University of Amsterdam); Elise van der Pol (Microsoft Research); Rita Cucchiara (Università di Modena e Reggio Emilia); Pascal Mettes (University of Amsterdam) CLOOME: Contrastive Learning for Molecule Representation with Microscopy Images and Chemical Structures Ana Sanchez-Fernandez (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)*; Elisabeth Rumetshofer (JKU Linz); Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Guenter Klambauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) Boosting Multi-modal Contrastive Learning with Modern Hopfield Networks and InfoLOOB Andreas Fürst (JKU Linz); Elisabeth Rumetshofer (JKU Linz)*; Johannes Lehner (Johannes Kepler University); Viet T. Tran (Johannes Kepler University Linz); Fei Tang (Here Technologies); Hubert Ramsauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); David Kreil (Institute of Advanced Research in Artificial Intelligence (IARAI) GmbH); Michael K Kopp (Institute of Advanced Research in Artificial Intelligence (IARAI) GmbH); Guenter Klambauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Angela Bitto-Nemling (JKU); Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) Global-based Deep Q-Network for Molecule Generation Asmaa Rassil (Faculty of science, University Chouaib Doukkali)*; Hiba Chougrad (University Sidi Mohamed Ben Abdellah); Hamid Zouaki (university Chouaib Doukkali) A Semantically Conditioned Code-Mixed Natural Language Generation for Task-Oriented Dialog Suman Dowlagar (International Institute of Information Technology-Hyderabad)*; Radhika Mamidi (IIIT Hyderabad) Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting Chandana Priya Nivarthi (University of Kassel)*; Stephan Vogt (University of Kassel); Bernhard Sick (University of Kassel) Modern Hopfield Networks for Iterative Learning on Tabular Data Bernhard Schäfl (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Lukas Gruber (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Angela Bitto-Nemling (JKU)*; Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) Delirium Prediction using Long Short-Term Memory (LSTM) in the Electronic Health Record Siru Liu (Vanderbilt University Medical Center)*; Joseph Schlesinger (Department of Anesthesiology, Division of Critical Care Medicine, Vanderbilt University Medical Center); Allison McCoy (Department of Biomedical Informatics, Vanderbilt University Medical Center); Thomas Reese (Department of Biomedical Informatics, Vanderbilt University Medical Center); Bryan Steitz (Department of Biomedical Informatics, Vanderbilt University Medical Center); Elise Russo (Department of Biomedical Informatics, Vanderbilt University Medical Center); Adam Wright (Department of Biomedical Informatics, Vanderbilt University Medical Center) Evaluating and Improving Robustness of Self-Supervised Representations to Spurious Correlations Kimia Hamidieh (University of Toronto, Vector Institute)*; Haoran Zhang (MIT); Marzyeh Ghassemi (University of Toronto, Vector Institute) FACTORS INFLUENCING POSTGRADUATE STUDENTS' ACADEMIC PERFORMANCE: MACHINE LEARNING APPROACH. Ayodele Esther Awokoya (University of ibadan)* A Simple Phoneme-based Error Simulator for ASR Error Correction Mohita Chowdhury (Ufonia Limited)*; Oliver Gardiner (Ufonia Limited); Yishu Miao (Ufonia Limited) Deep Learning methods for biotic and abiotic stresses detection in fruits and vegetables: state of the art and perspectives Ariane Houetohossou (University of Abomey-Calavi)*; Ratheil Vinasetan HOUNDJI (2Institut de Formation et de Recherche en Informatique, University of University of Abomey-Calavi); Castro Gbêmêmali HOUNMENOU (Laboratoire de Biomathématiques et d’Estimations Forestières, Faculty of Agronomic Sciences, University of Abomey-Calavi); Rachidatou SIKIROU (Laboratoire de Défense des Cultures, Centre de Recherches Agricoles d’Agonkanmey, Institut National des Recherches Agricoles du Bénin (INRAB)); Romain Lucas GLELE KAKAÏ (Laboratoire de Biomathématiques et d’Estimations Forestières, Faculty of Agronomic Sciences, University of Abomey-Calavi) Follow the Flow: An Affective Computing Interface for the On-Line Detection of Flow Mental State Elena Sajno (Università di Pisa)*; G. Riva (Catholic University of Milan, Italy); Nicole Novielli (University of Bari) Under-Counted Tensor Completion with Neural Network-based Side Information Learner Shahana Ibrahim (Oregon State University)*; Xiao Fu (Oregon State University); Rebecca Hutchinson (Oregon State University); Eugene Seo (Brown University) Detecting State Changes in Dynamic Neuronal Networks Yiwei Gong (UT Austin)*; Sinead A Williamson (UT Austin) Learning to Defer in Ranking Systems Aparna Balagopalan (MIT)*; Haoran Zhang (MIT); Elizabeth Bondi-Kelly (MIT); Thomas Hartvigsen (MIT); Marzyeh Ghassemi (University of Toronto, Vector Institute) Categorizing Online Harassment on Twitter using Graph Convolutional Networks Mozhgan Saeidi (Stanford University)* A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering Yu Qin (Tulane University)*; Brittany Fasy (Montana University); Carola Wenk (Tulane University); Summa Brian (Tulane University) A Recommendation System in Task-Oriented Doctor-Patient Interactions Suman Dowlagar (International Institute of Information Technology-Hyderabad)*; Radhika Mamidi (IIIT Hyderabad) Explaining Predictive Uncertainty by Looking Back at Model Explanations Hanjie Chen (University of Virginia)*; Wanyu Du (University of Virginia); Yangfeng Ji (University of Virginia) DaME: Data Mapping Engine for Financial Services Shubhi Asthana (IBM Research - Almaden)*; Ruchi Mahindru (IBM Watson Research Center) Improved Text Classification via Test-Time Augmentation Helen Lu (Massachusetts Institute of Technology (MIT))*; Divya Shanmugam (MIT); Harini Suresh (MIT); John Guttag (MIT) Model Interpretation based Sample Selection in Large-Scale Conversational Assistants Kiana Hajebi (Amazon Alexa AI)* Human-AI Interaction in Selective Prediction Systems Elizabeth Bondi-Kelly (MIT)*; RAPHAEL KOSTER (DeepMind); Hannah Sheahan (DeepMind); Martin Chadwick (DeepMInd); Yoram Bachrach; Taylan Cemgil (DeepMind); Ulrich Paquet (DeepMind); Krishnamurthy Dvijotham (DeepMind) User-interactive, On-demand Cycle-GAN-Based Super Resolution and Focus Recovery on Whole Slide Images (WSI) Huimin Zhuge (Tulane University)*; Summa Brian (Tulane University); J.Quincy Brown (Tulane University) Self Supervised Learning in Microscopy Aastha Jhunjhunwala (NVIDIA)*; Siddha Ganju (Nvidia) Machine Learning for the detection of diabetic retinopathy Francisca O Oladipo (Thomas Adewumi University)*; Taiwo Amusan (Federal University Lokoja) Topic: Building Identification In Aerial Imagery using Deep learning Proscovia Nakiranda (Stellenbosch University)* Dynamic Head Pruning in Transformers Prisha Satwani (The London School of Economics and Political Science )*; yiren zhao (University of Cambridge); Vidhi Lalchand (University of Cambridge ); Robert Mullins (University of Cambridge) Mobile-PDC: High-Accuracy Plant Disease Classification for Mobile Devices. Samiiha Nalwooga (Stockholm University)*; Henry Mutegeki (Makerere University ) CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks Sakshi Varshney (IIT Hyderabad)*; Vinay K Verma (IIT Kanpur); Srijith PK (IIT, Hyderabad, India); Piyush Rai (IIT Kanpur); Lawrence Carin Duke (CS) Modeling Sharing Time Of Fake And Real News Maya Zeng (Boise State University)*; Cooper Doe (Colorado College); Vladimir Knezevic (City College of San Francisco); Francesca Spezzano (Boise State University); Liljana Babinkostova (Boise State University) Interaction Classification with Key Actor Detection in Multi-Person Sport Videos Farzaneh Askari (University of McGill)*; Rohit Ramaprasad (Birla Institute of Technology and Science); James Clark (McGill University); Martin Levine (McGill University) Estimating Fairness in the Absence of Ground-Truth Labels Michelle Bao (Stanford University)*; Jessica Dai (UC Berkeley); Keegan Hines (Arthur AI); John Dickerson (Arthur AI) Motor Imagery ECoG Signal Classification With Optimal Selection Of Minimum Electrodes Tuga Abdelkarim Ahmed (nile center for technology research)*; Shubham Kumar (.); Ruoqi Huang (.) A Noether's theorem for gradient flow: Continuous symmetries of the architecture and conserved quantities of gradient flow Bo Zhao (University of California, San Diego)*; Iordan Ganev (Radboud University); Robin Walters (Northeastern University); Rose Yu (UC San Diego); Nima Dehmamy (IBM Research) Generating High-Quality Emotion Arcs Using Emotion Lexicons Daniela Teodorescu (University of Alberta)*; Saif Mohammad (National Research Council, Canada) Exposure Fairness in Music Recommendation Rebecca Salganik (Universite de Montreal )*; Fernando Diaz (Google); Golnoosh Farnadi (Mila, HEC Montreal, Université de Montréal) DeepWear: Towards an Automated Textiles Materials Classification using a Taxonomy-based ML Approach Shu Zhong (University College London)*; Miriam Ribul (Royal College of Art); Youngjun Cho (University College London); Marianna Obrist (University College London) Revisiting Graph Neural Network Embeddings Skye Purchase (University of Cambridge)*; yiren zhao (University of Cambridge); Robert Mullins (University of Cambridge) Estimating the Treatment Effect of Antibiotics Exposure on the Risk of Developing Anti-Microbial Resistance Hyewon Jeong (MIT)*; Kexin Yang (Harvard School of Public Health); Ziming Wei (Harvard School of Public Health); Yidan Ma (Harvard School of Public Health); Intae Moon (MIT); Sanjat Kanjilal (Harvard Medical School) Can we explain Aha! moments in artificial agents ? Ikram Chraibi Kaadoud (IMT Atlantique)*; Adrien Bennetot (Segula Technologies - Sorbonne Université - Ensta ParisTech); Barbara Mawhin (Human Factors Department, EBT-Salient Aero Foundation); Vicky Charisi (European Commission, Joint Research Center (JRC)); Natalia Diaz-Rodriguez (Department of Computer Science and Artificial Intelligence, DaSCI Andalusian Institute in Data Science and Computational Intelligence, University of Granada) Multimodal Deep Learning for Weapon Detection Parie R Desai (Marietta High School)*; Prajwal Saokar (Berry College); William Wansing (Mill Creek High School) The Role of Expert-driven Prompt Engineering for Fine-grained Zero-shot Classification in Fashion Dhanashree Balaram (Lily AI)*; Matthew Nokleby (LILY AI); Thiyagarajan Ramanathan (Lily AI); Ajitesh Gupta Gupta (Lily AI); Ravi Kannan (Lily AI) Explaining complex system of multivariate times series behavior Ikram Chraibi Kaadoud (IMT Atlantique)*; Lina Fahed (IMT Atlantique, Lab-STICC); Tian Tian (IMT Atlantique, Lab-STICC); Yannis Haralambous (IMT Atlantique); Philippe Lenca (IMT Atlantique) Computational models of Language Variation in Literary Narratives Krishnapriya Vishnubhotla (University of Toronto)* Graph Transformer Networks for Nuclear Proliferation Detection in Urban Environments Anastasiya Usenko (Pacific Northwest National Laboratory)*; Sameera Horawalavithana (Pacific Northwest National Laboratory); Ellyn Ayton (Pacific Northwest National Laboratory); Joon-Seok Kim (Pacific Northwest National Laboratory); Svitlana Volkova (Pacific Northwest National Laboratory) Automated Staging of Breast Cancer Histopathology Images Using Deep Learning. Angela M Crabtree (Providence Portland Medical Center (Earle A. Chiles Research Institute), University of Oregon)*; Narmada Naik (University Of Montreal); Kevin L Matlock (Omics Data Automation) Gaussian Process parameterized Covariance Kernels for Non-stationary Regression Vidhi Lalchand (University of Cambridge )*; Talay M Cheema (University of Cambridge); Laurence Aitchison (University of Bristol); Carl Edward Rasmussen (Cambridge University) Heart Disease Prediction Using Machine Learning Techniques Asegunloluwa E Babalola (Anchor University, Lagos)*; Tekena Solomon (Anchor University, Lagos) Multi-group Reinforcement Learning for Electrolyte Repletion Promise Osaine Ekpo (Princeton University)*; Barbara Engelhardt (Princeton University) Trading off Utility, Informativeness, and Complexity in Emergent Communication Mycal Tucker (MIT); Julie A. Shah (MIT); Roger Levy (Massachusetts Institute of Technology); Noga Zaslavsky (MIT)* Mitigating Online Grooming with Federated Learning Khaoula Chehbouni (HEC Montreal); Gilles Caporossi (HEC Montreal); Reihaneh Rabbany (McGill University)*; Martine De Cock (University of Washington Tacoma); Golnoosh Farnadi (Mila, HEC Montreal, Université de Montréal) Towards Private and Fair Federated Learning Sikha Pentyala (University of Washington, Tacoma)*; Nicola Neophytou (Mila); anderson nascimento (UW); Martine De Cock (University of Washington Tacoma); Golnoosh Farnadi (Mila, HEC Montreal, Université de Montréal) Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy Rachel Redberg (UC Santa Barbara); Yuqing Zhu (UC Santa Barbara)*; Yu-Xiang Wang (UC Santa Barbara) Characteristics of White Helmets Disinformation vs COVID-19 Misinformation Anika M Halappanavar (Pacific Northwest National Laboratory )*; Maria Glenski (Pacific Northwest National Laboratory) Biomedical Word Sense Disambiguation with Contextualized Representation Learning Mozhgan Saeidi (Stanford University)* Model Understanding and Debugging at The Level of Subpopulation Jun Yuan (New York University)* Fair Active learning by exploiting causal data structure Sindhu C M Gowda (University of Toronto)*; Haoran Zhang (MIT); Marzyeh Ghassemi (University of Toronto) Preference-Aware Constrained Multi-Objective Bayesian Optimization Alaleh Ahmadianshalchi (Washington State University)*; Syrine Belakaria (Washington State university); Janardhan Rao Doppa (Washington State University) Preliminary Study for Impact of Social Media Networks on Traffic Prediction Valeria Laynes Fiascunari (University of Central Florida)*; Luis Rabelo (University of Central Florida) Explanation-Guided Learning for Human-AI collaboration Silvia Tulli (Instituto Superior Técnico)* Trust Me Not: Trust Scoring for Continuous Model Monitoring Nandita Bhaskhar (Stanford University)*; Daniel Rubin (Stanford University); Christopher Lee-Messer (Stanford University) Multispectral Masked Autoencoder for Remote Sensing Representation Learning Yibing Wei (University of Wisconsin - Madison)*; Zhicheng Yang (PAII Inc.); Hang Zhou (PAII, Inc.); Mei Han (PAII Inc.); Pedro Morgado (University of Wisconsin-Madison); Jui-Hsin Lai (PAII Inc.) Learning Pedestrian Behaviour for Autonomous Vehicle Interactions Fanta Camara (University of Leeds)* Comparing neural population responses based on pairwise $p$-Wasserstein distance between topological signatures Liu Zhang (Princeton University)*; Fei Han (National University of Singapore); KELIN XIA (NANYANG TECHNOLOGICAL UNIVERSITY) Adversarial Analysis of Fake News Detectors Annat Koren (City College of San Francisco)*; Hunter Ireland (Boise State University); Sandra D Luo (Timberline High School); Eryn Jagelski-Buchler (Boise State University); Edoardo Serra (Boise State University); Francesca Spezzano (Boise State University) Fast Parameter Tuning for Rule-base Planners towards Human-like Driving Shu Jiang (Apollo Autonomous Driving )*; Szu-Hao Wu (Apollo Autonomous Driving) Model Averaging to Learn Bayesian Network Structures with Non-Linear Structured Representations Charupriya Sharma (University of Waterloo)* Augmenting Driver Decision-Making Using Meta-Inverse Reinforcement Learning Mayuree Binjolkar (University of Washington)*; Yana Sosnovskaya (University of Washington) Explaining black-box models in natural language through fuzzy linguistic summaries - Bipolar Disorder case study Olga Kaminska (Systems Research Institute Polish Academy of Sciences)*; Katarzyna Kaczmarek-Majer (SRI PAS) Physics-Constrained Deep Learning for Climate Downscaling Paula Harder (Fraunhofer ITWM)*; Qidong Yang (New York University); Venkatesh Ramesh (Mila); Prasanna Sattigeri (IBM Research); Alex Hernandez-Garcia (Mila - Quebec AI Institute); Campbell D Watson (IBM Reserch); Daniela Szwarcman (IBM Research); David Rolnick (McGill University, Mila) Graph Convolutional Neural Network-based Quality Assessment of Light Field Images Sana Alamgeer (Universidade de Brasilia, Brazil)* Erased text retrieval from historical palimpsest manuscripts using deep autoregressive priors Anna Starynska (Rochester Institute of Technology)*; David Messinger (Rochester Institute of Technology) Mask R-CNN model for banana diseases segmentation Neema Mduma (The Nelson Mandela African Institution of Science and Technology)*; Christian A Elinisa (The Nelson Mandela African Institution of Science and Technology) Security, IP protection, Privacy on Federated Learning and Machine Learning Edge Devices Mahdieh Grailoo (Tallinn University of Technology)* SOIL MINERAL DEFICIENCY DETECTION USING A DEEP LEARNING ALGORITHM COMMONLY KNOWN AS CONVOLUTIONAL NEURAL NETWORKS JEAN Mrs. AMUKWATSE (UTAMU)* P53 in Ovarian Cancer: Heterogenous Analysis of KeyBERT, BERTopic, PyCaret and LDAs methods Mary Adewunmi (UTAS,Hobart,Australia)*; Richard Oveh (Benson Idahosa University); Christopher Yeboah (PDM University); Solomon A Olorundare (University of Lagos); Ezeobi Peace (Mbarara University of Science and Technology ) Graph-Transformer for Cross-lingual Plagiarism Detection Oumaima Hourrane (University of Hassan II)* Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies Shachi Deshpande (Cornell University)*; Kaiwen Wang (Cornell University); Dhruv Sreenivas (Cornell University); Zheng Li (Cornell University); Volodymyr Kuleshov (Cornell University) A recommendation system for technology intelligence based on multiplex networks Foutse Yuehgoh (African institut for mathematical sciences )* Pre-processing of Social Media Feeds based on Integrated Local Knowledge Base Taiwo Kolajo (Department of Computer Science, Federal University Lokoja, Kogi State, Nigeria)*; Olawande Daramola (CPUT, Cape Town, South Africa ); Ayodele Adebiyi (Department of Computer Science, Landmark University) Attention-Augmented ST-GCN for Efficient Skeleton-based Human Action Recognition Negar Heidari (Aarhus University)*; Alexandros Iosifidis (Aarhus University) Leveraging artificial intelligence for automatic depression detection using speech recognition. Hewitt Tusiime (Makerere University)*; Alvin Nahabwe (Makerere University ); Julius Kimuli (Makerere University); Grace Babirye (Laboremus Uganda) Shapelet Guided Counterfactual Explanation Generation for Black-Box Time Series Classifiers Tina Han (ConsumerAffairs)*; Jette Henderson (CognitiveScale) Weakly Supervised Medical Image Segmentation with Soft Labels and Noise Robust Loss Banafshe BF Felfeliyan (University of Calgary)*; Abhilash Rakkunedeth (University of Alberta); Jacob Jaremko (University of Alberta); Janet Ronsky (University of Calgary) Human trafficking detection using lockstep behaviour methods Maricarmen MA Arenas (Mila)*; reihaneh rabbany (Mila); Golnoosh Farnadi (Mila) Improving Induced Valence Recognition by Integrating Acoustic Sound Semantics in Movies Shreya G Upadhyay (National Tsing Hua University)*; Bo-Hao Su (Department of Electrical Engineering, National Tsing Hua University); Chi-Chun Lee (Department of Electrical Engineering, National Tsing Hua University) Efficient Hospital Management via Length of Stay prediction using Domain Adaptation Lyse Naomi Momo Wamba (KU Leuven)*; Nyalleng Moorosi (Google); Elaine Nsoesie (Boston University); Frank Rademakers (UZ Leuven); Bart DeMoor (KU Leuven) Reinforcement Learning for Cost to Serve Pranavi Pathakota (TCS Research)*; Kunwar Zaid (TCS Research); Hardik Meisheri (TCS Research); Harshad Khadilkar (TCS Research) Dual Channel Training of Large Action Spaces in Reinforcement Learning Pranavi Pathakota (TCS Research)*; Hardik Meisheri (TCS Research); Harshad Khadilkar (TCS Research) Robustness in Weighted Networks Luisa Cutillo (University of Leeds)*; Valeria Policastro (National Research Council); Annamaria Carissimo (National Research Council) Mapping Slums with Machine Learning and Medium-Resolution Satellite Imagery Agatha Mattos (University College Dublin)*; Michela Bertolotto (University College Dublin); Gavin McArdle (University College Dublin) Investigating the Effects of Environmental Factors on the Detection of Laryngeal Cancer from Speech Signals Using Machine Learning Mary L Paterson (University of Leeds)*; Luisa Cutillo (University of Leeds); James Moor (Leeds Teaching Hospitals NHS Trust) 3D-LatentMapper: View Agnostic Single-View Reconstruction of 3D Shapes Alara Dirik (Bogazici University)*; Pinar Yanardag (Bogazici University) Toward Qualitative Mechanical Problem-Solving using Hybrid AI Shreya Bhowmick Banerjee (Rensselaer Polytechnic Institute)*; Selmer Bringsjord (Rensselaer Polytechnic Institute); Naveen Govindarajulu (RPI) Fair Targeted Immunization with Dynamic Influence Maximization Nicola Neophytou (Mila)*; Golnoosh Farnadi (Mila, HEC Montreal, Université de Montréal) You Only Live Once: Single-Life Reinforcement Learning via Learned Reward Shaping Annie S Chen (Stanford University)*; Archit Sharma (); Sergey Levine (UC Berkeley); Chelsea Finn (Stanford) Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time Caroline Choi (Stanford University)*; Huaxiu Yao (Stanford University); Yoonho Lee (Stanford University); Pang Wei Koh (Stanford University); Chelsea Finn (Google) Probabilistic Querying of Continuous-Time Sequential Events Alex J Boyd (UC Irvine); Yuxin Chang (University of California, Irvine)*; Stephan Mandt (University of California, Irivine); Padhraic Smyth (University of California, Irvine) Resume Parsing using an ensemble of CNN, Bi-LSTM and CRF in a Hard Voting Predictive Approach Scholastica N Mallo (Nigeria Defence Academy)*; Francisca Nonyelum Ogwueleka (Department of Computer Science Nigerian Defence Academy Kaduna, Nigeria); Philip Odion (Nigeria Defence Academy); Martin Irhebhude (Nigeria Defence Academy, Kaduna) Transformers for Synthesized Speech Detection Emily Bartusiak (Purdue University)* Polynomials in Bayesian Problems Lilian Wong (Borealis AI)*; Evans Harrell (Georgia Tech) Estimating Uncertainty in Safety-Critical Deep Learning Models Oishi Deb (University of Oxford)* Adaptive Temporal Pattern Matching Sepideh Koohfar (University of New Hampshire)* Respiratory Conditions (EIPH, PLH, and Mucus) in Racehorses Allison B Fisher (Washington State University)*; Warwick Bayly (Washington State University); Sierra Shoemaker (Washington State University); Julia Bagshaw (Washington State University); Yuan Wang (Washington State University); Macarena Sanz (Washington State University) Shared Hardware, Shared Baselines: An Offline Robotics Benchmark Gaoyue Zhou (CMU)*; Victoria Dean (CMU) Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics Noga Mudrik (The Johns Hopkins University)*; Yenho Chen (Georgia Institute of Technology); Eva Yezerets (The Johns Hopkins University); Christopher J Rozell (Georgia Institute of Technology); Adam Charles (Johns Hopkins University) Probabilistic Interactive Segmentation for Medical Images Hallee E Wong (MIT)*; John Guttag (MIT); Adrian V Dalca (MIT) Evaluation of Active Learning and Domain Adaptation on Health Data Kristina Holsapple (University of Delaware)*; Haoran Zhang (MIT); Marzyeh Ghassemi (University of Toronto, Vector Institute) Towards interpretable health monitoring and service anomaly detection in the cloud Yueying Li (Cornell)*; Edward Suh (Cornell University); Christina Delimitrou (Cornell) Hearing Touch: Using Contact Microphones for Robot Manipulation Shaden N Alshammari (Massachusetts Institute of Technology)*; Victoria Dean (CMU); Tess Hellebrekers (Meta AI); Pedro Morgado (University of Wisconsin-Madison); Abhinav Gupta (Carnegie Mellon University Robotics Institute) Adapting the Function Approximation Architecture in Online Reinforcement Learning John D Martin (University of Alberta); Joseph Modayil (DeepMind); Fatima Davelouis Gallardo (University of Alberta)*; Michael Bowling (University of Alberta) FMAM: A novel Factorization Machine based Attention Mechanism for Forecasting Time Series Data Fahim T Azad (Arizona State University)* The WiML Board and Committee would like to thank all the reviewers that helped: Adriana Romero-Soriano (FAIR) Alaa Bessadok (University of Sousse, Tunisia) Amita Misra (IBM) Angelica Aviles-Rivero (University of Cambridge) Ankita Shukla (ASU) Anna Klimovskaia Susmelj (Swiss Data Science Center) Asra Aslam (Insight Centre for Data Analytics, Ireland) Beyza Ermis (Boğaziçi University) Bingshan Hu (University of Alberta) Bo Dong (Amazon) Celestine Mendler-Dünner (Max Planck Institute for Intelligent Systems, Tübingen) Claire Vernade (Deepmind) Dalin Guo (UC San Diego; Twitter, Inc.) Deepika Bablani (IBM Research) Erin Grant (UC Berkeley) Gowthami Somepalli (University of Maryland, College Park) Han Shao (Toyota Technological Institute at Chicago) Hanjie Chen (University of Virginia) Ilke Demir (Intel Corporation) Isabela Albuquerque (DeepMind) Ishita Mediratta (BITS Pilani K.K. Birla Goa Campus) Itir Onal Ertugrul (Utrecht University) Kasturi Bhattacharjee (AWS AI, Amazon) Kavya Gupta (Centralesupelec) Kuan-Ting Chen (National Taiwan University) Maria Glenski (Pacific Northwest National Laboratory) Maria Lomeli (Meta) Mayoore Jaiswal (University of Washington) Mengjiao Wang (Amazon Visual Search) Mina Ghadimi Atigh (University of Amsterdam) Minhae Kwon (Soongsil University) Naga Vara Aparna Akula (CSIR-CSIO) Natalia Efremova (Queen Mary University, London) Nesime Tatbul (Intel Labs and MIT) Niha Beig Case (Western Reserve University) Nora Hollenstein (University of Copenhagen) Obioma Pelka (University of Applied Sciences and Arts Dortmund) Ozge Nilay Yalcin (Simon Fraser University) Pascale Gourdeau (University of Oxford) Peixian Liang (University of Notre Dame) Priyadarshini Kumari (Sony AI) Rania Ibrahim (Purdue University) Sakinat Folorunso Olabisi Onabanjo university Samira Daruki (Expedia Research) Sanae Lotfi (New York University) Sandareka Wickramanayake (National University of Singapore) Sandya mannarswamy (Intel India) Sara Magliacane (University of Amsterdam) Sasha Luccioni (Mila) Shahana Ibrahim (Oregon State University) Shailee Jain (The University of Texas at Austin) Shimeng Peng (Nagoya university) Shinjini Ghosh (Massachusetts Institute of Technology) Shuai Zhang (Amazon) Sinem Aslan (Ca' Foscari University of Venice) Siru Liu (Vanderbilt University Medical Center) Sonali Agarwal (IIIT-Allahabad) Subarna Tripathi (Intel Labs) Surangika Ranathunga (University of Moratuwa) Svitlana Volkova (Pacific Northwest National Laboratory) Syrine Belakaria (Washington State university) Tania Lorido-Botran (Independent Researcher) Utkarshani Jaimini (Artificial Intelligence (AI) Institute- University of South Carolina) Veronika Cheplygina (ITU) Vidhi Lalchand (University of Cambridge) Vishwali Mhasawade (New York University) Weiyan Shi (Columbia University) Xi Rao (ETH) Xiao Zhang (T-Mobile) Xinyi Chen (Google) Xun Tang (Yelp) Yao Qin (University of California, San Diego) Yixin Wang (University of Michigan) Call for Participation The 17th Workshop for Women in Machine Learning (WiML) will be co-located with NeurIPS in New Orleans, Louisiana and will be hybrid. The NeurIPS workshop for Women in Machine Learning will be held in person on Monday November the 28th and virtually on Monday December the 5th with invited speakers, oral presentations, and posters. The event brings together members of the academic and industry research landscape for an opportunity to connect and exchange ideas, and learn from each other. There will be a mentoring session to discuss current research trends and career choices in machine learning. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. All presenters should be women or non-binary, and all genders are invited to attend. All submissions must abide by the WiML Code of Conduct. Submission page: https://cmt3.research.microsoft.com/WiML2022 Notification of acceptance is now sent to authors. Authors with accepted su bmissions and with a submitted travel funding application will be receiving further communication. IMPORTANT DATES August 1st, 2022 11:59pm PT – Abstract Submission Open on CMT August 26th, 2022 11:59pm PT – Abstract Submission Deadline September 1st, 2022 11:59 PT - Abstract Submission Deadline [extended] September 8th, 2022 11:59pm PT - Travel funding Application Deadline September 15th, 2022 11:59pm PT – Notification of Acceptance September 16th, 2022 11:59pm PT – Notification of Travel Funding November 21st, 2022 11:59pm PT – Registration Deadline for NeurIPS November 28th, 2022 – WiML Workshop Day (in person) December 5th, 2022 – WiML Workshop Day (virtual) SUBMISSION INSTRUCTIONS We strongly encourage students, postdocs, and researchers in all areas of machine learning who are women or non-binary to submit an abstract (1 page PDF) describing new, previously, or concurrently published research. We welcome abstract submissions in theory, methodology, as well as applications. While the presenting author need not be the first author of the work, we request that the presenting author should be woman or non-binary. Submissions will be reviewed in a double-blind setting. Authors of accepted abstracts will be asked to present their work in either a virtual or in-person poster session. A few authors will be selected to give oral presentations. There are no formal proceedings. Abstracts are non-archival: they may describe completed research or work-in-progress. Please refer to the detailed Submission Instructions . TRAVEL FUNDING Registration to the NeurIPS conference is required to participate in this year's WiML workshop. Travel funding will be available for eligible WiML participants, to help cover transportation, meals, accomodation, poster printing and/or visa application related costs. The funding amounts will depend on the geographic location of the qualified recipients and their types of needs. Travel funding recipients are required to volunteer during the WiML Workshop. To qualify, the participant must: i) be a woman or non-binary, ii) be the presenting and primary author of an accepted abstract at WiML 2022, and iii) be a student, postdoc, or hold an equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented geographical areas). WiML travel funding is administered as reimbursements after the workshop and no funding is allocated before the workshop. If you are attending NeurIPS, we also encourage you to apply for NeurIPS’ volunteering and travel funding opportunities, which are separate and independent of WiML travel funding. More details can be found on the NeurIPS website . Travel funding application form [now CLOSED]: Authors that have submitted their work will receive the application form starting on September 2, 2022. The application deadline is September 8th, 2022. Applications received past this deadline will not be considered. * Important Note * More information on funding opportunities can be found in our FAQ section of our webpage. AREA CHAIRS Area chairs should be women or non-binary. The role of area chairs is to write a review and suggest an accept/reject decision for each abstract. We expect each area chair to be responsible for up to 10 one-page abstracts. Area chair application form [now CLOSED]: If you are interested in being an area chair, please apply via the application link . Update: the deadline for the AC application has now passed (deadline August 24th). Thank you for your interest. Visa invitation letter Information on visa invitation letter requests can be found in our FAQ section of our webpage. ORGANIZERS Sergul Aydore (Amazon AI) Gloria Namanya (Makerere AI Research) Mariam Arab (Microsoft and Simon Fraser University) Beliz Gunel (Google AI) Kimia Nadjahi (MIT) Konstantina Palla (Spotify Research) Questions? Check out the FAQs or reach us at workshop[at]wimlworkshop[dot]org PLATINUM SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS SUPPORTERS Committee ORGANIZERS Sergül Aydöre General Chair Konstantina Palla Senior Program Chair Gloria Namanya Finance and Sponsorship Chair Beliz Gunel Mentorship Chair Mariam Arab Logistics Chair Kimia Nadjahi Student Program and Funding Chair Code of Conduct The open exchange of ideas, the freedom of thought and expression, and respectful scientific debate are central to the goals of Women in Machine Learning, Inc. (“WiML”) activities; this requires a community and an environment that recognizes and respects the inherent worth of every person. The purpose of this Code of Conduct (CoC) is to outline expected standards of behavior during WiML activities. Scope This CoC applies to all WiML activities, including but not limited to: Events organized, hosted, co-branded, or in cooperation with WiML Submissions and reviewing processes run by WiML. Communications are sent through communication channels associated with WiML, including but not limited to social media. Meetings and discussions associated with WiML activities. If an activity is in cooperation with another organization, if the other organization has its own CoC, the union of both CoCs apply. Responsibility All attendees, speakers, mentors, panelists, area chairs, reviewers, sponsors, contractors, organizers, volunteers, members of the WiML Board of Directors and Senior Advisory Council (referred to as “Participants” collectively throughout this document) involved in WiML activities as described above are required to comply with this CoC. Reviews should actively avoid subtle discrimination, however inadvertent. In particular, reviewers should avoid comments in reviews about English style or grammar that may be interpreted as implying that the author is “foreign” or “non-native”. Sponsors are equally subject to this CoC. In particular, sponsors should not use images, activities, or other materials that reinforce gender stereotypes or are of a sexual, racial, or otherwise offensive nature at WiML events. Booth staff, including but not limited to volunteers, should not create a sexualized environment. Unacceptable Behavior WiML is dedicated to providing an experience for all participants that is free from harassment, bullying, discrimination, and retaliation. This includes offensive comments related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), politics, technology choices, or any other personal characteristics or considerations made unlawful by federal, state, or local laws, ordinances, or regulations. Inappropriate or unprofessional behavior that interferes with another participant’s full participation will not be tolerated. This includes bullying, intimidation, personal attacks, harassment, sustained disruption of talks or other events, sexual harassment, stalking, following, harassing photography or recording, inappropriate physical contact, unwelcome sexual attention, public vulgar exchanges, derogatory name-calling, or diminutive characterizations, all of which are unwelcome in this community. Advocating for, or encouraging, any of the above behavior, is also considered harassment. No use of images, activities or other materials that are of a sexual, racial, or otherwise offensive nature that may create an inappropriate or toxic environment is permitted. Disorderly, boisterous, or disruptive conduct including but not limited to fighting, coercion, theft, damage to property, or any mistreatment or non-businesslike behavior towards other participants is not tolerated. Scientific misconduct—including but not limited to fabrication, falsification, or plagiarism of paper submissions or research presentations—is prohibited. Reporting If you have concerns related to your participation or interaction at a WiML activity, observe someone else’s difficulties, or have any other concerns you wish to share, you can make a report: Anytime: By email at wiml.code.of.conduct@gmail.com ‬ During an event: In-person to organizers, volunteers, or any member of the WiML Board of Directors. They will then direct you to the designated responder(s) for that event. Organizers and volunteers can be identified by special badges marked as “ORGANIZER” or “VOLUNTEER”. Members of the WiML Board of Directors can be identified by special badges marked as “WiML Board”. There is no deadline by which to make a report. If the person receiving your report is not the designated responder for that event, they will direct you to a designated responder and/or provide you immediate medical or security help and assist you to feel safe for the duration of the activity. Designated responders will follow WiML procedures to respond to and investigate your report. Enforcement Any participant asked by any member of the community to stop any unacceptable behavior is expected to comply immediately. A response of “just joking” will not be accepted; behavior can be harassing without an intent to offend. If a participant engages in behavior that violates this CoC, WiML retains the right to take any action deemed appropriate, including but not limited to: Formal or informal warnings Barring or limiting continued attendance and participation, including but not limited to expulsion from the event Barring from participating in or deriving benefits from future WiML activities Exclusion from WiML opportunities, e.g. leadership, organizing, volunteering, speaking, reviewing, sponsoring, etc. Reporting the incident to the offender’s local institution or funding agencies Reporting the incident to local law enforcement The same actions may be taken toward any individual who engages in retaliation or who knowingly makes a false allegation of harassment. If action is taken, an appeals process will be made available. Investigation Reports of violations will be handled at the discretion of the WiML Board of Directors, who will investigate reports and bring the issue to resolution. Reports made during the activity will be responded to within 24 hours; reports made at other times will be responded to in less than five weeks. All reports will be handled as confidentially as possible and information will be disclosed only as it is necessary to complete the investigation and bring to resolution. There may be situations (such as those involving Title IX issues in the United States and venue- or employer-specific policies) where the member of the WiML Board of Directors informed of the violation will be under an obligation to file a report with another individual or organization outside of WiML. Ongoing Review The WiML Board of Directors welcomes feedback from the community on this CoC policy and procedures; please contact us by email at info@wimlworkshop.org . Acknowledgments This CoC policy was written by adapting the wording and structure from other CoC policies and procedures by Geek Feminism Wiki (created by the Ada Initiative), NeurIPS , ACM , Montreal AI Symposium , and Deep Learning Indaba . FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here ! What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 3rd Women in Machine Learning Un-Workshop, ICML 2022 The 3rd WiML Un-Workshop is co-located with ICML on Monday, July 18th, 2022. Speakers Logistics Breakout Sessions Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications, it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning, primarily intended to foster active participant engagement in the program. This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. Now in its 3th year, the 2022 un-workshop is co-located with ICML . Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch or social at the AISTATS or AAAI conferences, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Emma Brunskill Emma Brunskill is an associate professor in the Computer Science Department at Stanford University. Her goal is to create AI systems that learn from few samples to robustly make good decisions, motivated by our applications to healthcare and education. Her work has been honored by early faculty career awards (National Science Foundation, Office of Naval Research, Microsoft Research) received several best research paper nominations (CHI, EDMx3) and awards (UAI, RLDM, ITS). Celestine Mendler-Dünner Celestine Mendler-Dünner is a research group lead at the Max Planck Institute for Intelligent Systems in Tübingen. Her research focuses on the role of society in the study of computation, taking into account actions and reactions of individuals when analyzing and designing algorithmic systems. Prior to joining MPI-IS Celestine was a SNSF postdoctoral fellow at UC Berkeley, and a predoctoral researcher at IBM Research Zurich. She obtained her PhD from ETH Zurich where she was awarded the ETH medal and the Fritz Kutter prize for the academic as well as the industrial impact of her research. Yixin Wang Yixin Wang is an LSA Collegiate Fellow and an assistant professor of statistics at the University of Michigan. She works in the fields of Bayesian statistics, machine learning, and causal inference. Her research has received several awards, including the INFORMS data mining best paper award, Blackwell-Rosenbluth Award from the junior section of ISBA, student paper awards from ASA Biometrics Section and Bayesian Statistics Section, and the ICSA conference young researcher award. Location This workshop will be hybrid, co-located with ICML at the Baltimore Convention Center , Baltimore, Maryland USA. Type of registration required to attend Any type of in-person registration (tutorial / workshop / conference / all) grants you in-person access to the un-workshop. Also, an in-person registration includes access to the virtual one. Breakout Sessions Breakout Sessions During the day of the WiML Un-Workshop @ ICML 2022 there will be three different Breakout Sessions. We list the sessions, topics, and leaders. BreakoutGhoshehBreakout Session #1 (9.10AM - 10.10AM) IN-PERSON Breakout Sessions Machine learning real-time applications in health. Leader: Dania Humaidan, Co-leader: Cansu Sen. VIRTUAL Breakout Sessions Deep Generative Models for Electronic Health Records. Leader: Ghadeer Ghosheh, Co-leader: Tingting Zhu. Affective Computing: A Computational Perspective. Leader: Shreya Ghosh, Co-lead: Garima Sharma. Introducing geometry awareness in deep networks. Leader: Ankita Shukla. Breakout Session #2 (11.05AM - 12.05AM) IN-PERSON Breakout Sessions Challenges and opportunities in certified auditing of ML models. Leader: Chhavi Yadav. Robustness of Deep Learning Models to Distribution Shift. Leader: Polina Kirichenko, Co-leads: Shiori Sagawa, Sanae Lofti. VIRTUAL Breakout Sessions Knowledge Distillation through the lense of the capacity gap problem. Leader: Ibtihel Amara, Co-lead: Samrudhdhi Rangrej, Zahra Vaseqi. Improving AI Education. Leader: Mary Smart, Co-lead: Stefania Druga. Statistical Inference & Applications to Machine Learning. Leader: Lilian Wong, Co-lead: Po-ling Loh. Breakout Session #3 (15.25 - 16.25) IN-PERSON Breakout Sessions Robustness of Machine Learning. Leader: Yao Qin Towards efficient and robust deep learning training. Leader: Wenhan Xia. VIRTUAL Breakout Sessions Machine Learning for Physical Sciences. Leader: Taoli Cheng. Limitations of explainable/interpretable AI: frontiers and boundaries for future advancement. Leader: Haoyu Du, Co-lead: Peiyuan Zhou, Annie Lee, Rainah Khan. Detection of Unseen Classes of different Domains using Computer Vision. Leader: Asra Aslam. PROGRAM PANELISTS IN-PERSON MENTORS VIRTUAL MENTORS POSTERS The program follows the following color scheme: talks , breakout sessions , poster sessions , mentoring sessions , program break , sponsor talks , panel discussion . All invited talk titles, and invited speaker/mentor/panelist names are *clickable*. The majority of the program will be streamed and occur synchronously in-person and virtually, except if marked as in-person/virtual only. You can find the zoom links and livestream on the WiML workshop page of the ICML website . 08:30 Introduction & Opening Remarks , Vinitra Swamy all-day Virtual Sponsor Booths , [DeepMind, D.E. Shaw Research, Home Depot, Microsoft Research] all-day In-Person Sponsor Booths , [DeepMind, Google, QuantumBlack] 08:45 Desiderata for Representation Learning: A Causal Perspective , Yixin Wang [Invited Talk] Abstract: Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data like images and texts. Ideally, such a representation should efficiently capture non-spurious features of the data. It shall also be disentangled so that we can interpret what feature each of its dimensions capture. However, these desiderata are often intuitively defined and challenging to quantify or enforce. In this talk, we take on a causal perspective of representation learning. We show how desiderata of representation learning can be formalized using counterfactual notions, enabling metrics and algorithms that target efficient, non-spurious, and disentangled representations of data. We discuss the theoretical underpinnings of the algorithm and illustrate its empirical performance in both supervised and unsupervised representation learning. Joint work with Michael Jordan . 09:10 Breakout session [in-person only] Machine learning real-time applications in health (Leaders: Dania Humaidan, Cansu Sen) [hybrid] Introducing geometry awareness in deep networks (Leader: Ankita Shukla) [hybrid] Affective Computing: A Computational Perspective (Leaders: Shreya Ghosh, Garima Sharma) [hybrid] Deep Generative Models for Electronic Health Records (Leaders: Ghadeer Ghosheh) 10:10 Poster Session 10:40 Emma Brunskill [Invited Talk] 11:05 Breakout session [in-person only] Challenges and opportunities in certified auditing of ML models (Leader: Chhavi Yadav) [in-person only] Robustness of Deep Learning Models to Distribution Shift (Leaders: Polina Kirichenko, Shiori Sagawa) [hybrid] Knowledge Distillation through the Lens of the Capacity Gap Problem (Leaders: Ibtihel Amara, Samrudhdhi Rangrej, Zahra Vaseqi) [hybrid] Improving AI Education (Leaders: Mary Smart, Stefania Druga) [hybrid] Statistical Inference & Applications to Machine Learning (Leaders: Lilian Wong, Po-ling Loh) 12:05 Mentoring Roundtables [in-person only] /// Mentoring Panel [virtual only] Table 1: Choosing between academia and industry Amy Zhang & Lauren Gardiner Mentors: Jigyasa Grover , Ciara Pike-Burke, Nika Haghtalab, Po-Ling Loh, Hermina Petric Maretic Table 2: Finding mentors and taking on mentorship roles throughout your career / Celestine Mendler-Dünner & Cyril Zhang Moderator: Sinead Williamson Table 3: Establishing and maintaining collaborations Surbhi Goel & Max Simchowitz Table 4: Work-life Balance Ioana Bica & Kishore Kumar 13:05 Lunch Break, joint with NewInML [in-person only] /// Virtual Sponsor Booths [virtual only] 14:40 Harnessing the power of Hybrid Intelligence, Maria Olivia Lihn [QuantumBlack Sponsor Talk] 14:55 Building embodied agents that can learn from their environments and humans, Kavya Srinet [Meta Platforms Sponsor Talk] 15:10 Machine Learning at Apple, Tatiana Likhomanenko [Apple Sponsor Talk] 15:25 Breakout session [in-person only] Robustness of Machine Learning (Leader: Yao Qin) [in-person only] Distributionally robust Reinforcement Learning (Leaders: Laixi Shi, Mengdi Xu) [hybrid] Machine Learning for Physical Sciences (Leader: Taoli Cheng) [hybrid] Limitations of explainable/interpretable AI: frontiers and boundaries for future advancement (Leaders: Haoyu Du, Peiyuan Zhou, Annie Lee, Rainah Khan) [hybrid] Detection of Unseen Classes of different Domains using Computer Vision (Leader: Asra Aslam) 16:30 Poster Session, joint with LXAI 17:00 Social dynamics in prediction, Celestine Mendler-Dünner [Invited Talk] Abstract: Algorithmic predictions inform consequential decisions, incentivize strategic actions, and motivate precautionary measures. As such, predictions used in societal systems not only describe the world they aim to predict, but they have the power to change it; a prevalent phenomenon often neglected in theories and practices of machine learning. In this talk, I will introduce a risk minimization framework, called performative prediction, that conceptualizes this phenomenon by allowing the predictive model to influence the distribution over future data. This problem formulation elucidates different algorithmic solution concepts, optimization challenges, and offers a new perspective on prediction. In particular, I will discuss how performative prediction allows us to articulate the difference between learning from a population and steering a population through predictions, facilitating an emerging discourse on the topic of power of predictive systems in digital economies. 17:25 Best Practices for Research: Increasing Efficiency and Research Impact, and Navigating Hybrid Collaborations [Panel] Panelists: Amy Zhang , Surbhi Goel , Agni Kumar Moderator: Ioana Bica 18:25 Closing Remarks, Tatjana Chavdarova Note: Please navigate the 'Program' menu in the slidebar at the top to find more details about speakers, panelist and mentors. Surbhi Goel Surbhi Goel is currently a postdoctoral researcher at Microsoft Research NYC. In Spring 2023, she will be starting as the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania. Prior to this, she received her Ph.D. from the Department of Computer Science at the University of Texas at Austin where she was advised by Adam Klivans. Her work lies at the intersection of machine learning and theoretical computer science, with a focus on developing the statistical and computational foundations of modern machine learning paradigms. Among other honors, she is a recipient of UT Austin's Bert Kay Dissertation award, a J.P. Morgan AI PhD fellowship, and a Simons-Berkeley research fellowship. She has been recognized as a Rising Star in ML by University of Maryland and in EECS by UIUC. She is actively involved in service and outreach through her role as the co-founder of Learning Theory Alliance (LeT-All), a community building and mentorship initiative for the learning theory community. Amy Zhang Amy is a postdoctoral scholar at UC Berkeley and a research scientist at Facebook AI Research, and is starting as an assistant professor at UT Austin in the ECE department in Spring 2023. She works on state abstractions, model-based reinforcement learning, representation learning, and generalization in RL. She did her PhD at McGill University and Mila - Quebec AI Institute, co-supervised by Joelle Pineau and Doina Precup. She also has an M.Eng. in EECS and dual B.Sci. degrees in Mathematics and EECS from MIT. Agni Kumar Agni is an Applied Research Scientist on Apple’s Health AI team. She studied at MIT, graduating with an M.Eng. in Machine Learning and B.S. degrees in Mathematics and Computer Science. Her thesis on modeling the spread of healthcare-associated infections led to joining projects at Apple with applied health focuses, specifically on understanding cognitive decline from device usage data and discerning respiratory rate from wearable microphone audio. She has published hierarchical reinforcement learning research and predictive modeling work in conferences and journals, including CHIL, EMBC, PLOS Computational Biology, and Telehealth and Medicine Today. She was a workshop organizer for ICML’s first “Computational Approaches to Mental Health” workshop in 2021. She has also volunteered at WiML workshops and served as a reviewer for NeurIPS. For joy, Agni leads an Apple-wide global diversity network about encouraging mindfulness to find peace each day. Ioana Bica (Moderator) Ioana is a rising fifth-year PhD student at the University of Oxford and at the Alan Turing Institute, advised by Prof. Mihaela van der Schaar. Her PhD research focuses on building machine learning methods for improving and understanding decision making. To achieve this, she have worked on developing causal inference methods capable of estimating the individualized effect of interventions (e.g. actions or treatments) from observational data. Her research experience also includes an internship at DeepMind where she has been working with Jovana Mitrović on self-supervised learning and causality with the aim of learning better representations for objects in images. Prior to her PhD, she completed a Bachelor’s degree and a Master’s degree in Computer Science at the University of Cambridge where she worked with Prof. Pietro Liò on multi-modal data integration and unsupervised learning for genomics data. During this time, she has also interned at Google four times. Amy Zhang Amy is a postdoctoral scholar at UC Berkeley and a research scientist at Facebook AI Research, and is starting as an assistant professor at UT Austin in the ECE department in Spring 2023. She works on state abstractions, model-based reinforcement learning, representation learning, and generalization in RL Celestine Mendler-Dünner Celestine is a research group lead at the Max Planck Institute for Intelligent Systems in Tübingen. Her research focuses on the role of society in the study of computation, taking into account actions and reactions of individuals when analyzing and designing algorithmic systems. Surbhi Goel Surbhi is currently a postdoctoral researcher at Microsoft Research NYC. In Spring 2023, she will be starting as the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania. Prior to this, she received her Ph.D. from the Department of Computer Science at the University of Texas at Austin where she was advised by Adam Klivans. Her work lies at the intersection of machine learning and theoretical computer science, with a focus on developing the statistical and computational foundations of modern machine learning paradigms. Ioana Bica Ioana is a rising fifth-year PhD student at the University of Oxford and at the Alan Turing Institute, advised by Prof. Mihaela van der Schaar. Her research focuses on building machine learning methods for improving and understanding decision making. Lauren Gardiner Lauren is Senior Applied Research Scientist in the Health AI team at Apple. Cyril Zhang Cyril is a Senior Researcher at Microsoft Research NYC. His research interests include sequential prediction and decision-making, the theory and practice of optimization (especially in deep learning), and the synthesis of these topics (especially in language models). Max Simchowitz Max is a postdoc in Russ Tedrake's group at MIT. His recent work has focused on the theoretical foundations of online control and reinforcement learning, with past research ranging broadly across topics in adaptive sampling, multi-arm bandits, complexity of convex and non-convex optimization, and fairness in machine learning. He is currently interested in developing rigorous, theoretical guarantees for nonlinear control, wherever possible. Kishore Kumar Kumar is a Data Science and Analytics Lead at Amazon Prime video. He strives to solve complex business problems using advanced Machine Learning Algorithms, and has 10+ years of overall experience across multiple sectors. Nika Haghtalab Nika is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She works broadly on the theoretical aspects of machine learning and algorithmic economics. Ciara Pike-Burke Ciara is a Lecturer in Statistics in the Department of Mathematics at Imperial College London. Her research is in the field of statistical machine learning, particularly interested in sequential decision making problems. Hermina Petric Maretic Hermina is an Applied Scientist at Amazon working on time series forecasting. Her research interests include optimal transport, graphical models, network inference and interpretability. Po-Ling Loh Po-ling is a Lecturer in the Statistical Laboratory in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge. Her interests include high-dimensional statistics, optimization, network inference, and statistical applications to medical imaging and epidemiology. Jigyasa Grover Jigyasa is a Senior Machine Learning Engineer at Twitter working in the Online Ads Prediction & Ranking domain, where she is spearheading a variety of projects spanning ML model development, user tracking transparency remediations, and monetizing new Twitter products. Sinead Williamson (Moderator) Sinead is an Assistant Professor of Statistics at the University of Texas at Austin, in the Department of Statistics and Data Science. Her research interests include network analysis, scalable inference methods, and bayesian nonparametrics. Self-Similarity Priors: Neural Collages as Differentiable Fractal Representation s Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon [poster] Interpretable Adversarial Attacks using Frank Wolfe Tooba Imtiaz1, Morgan Kohler, Jared Miller, Octavia Camps, Mario Sznaier, Jennifer Dy [poster] Robust task-specific adaption of drug-target interaction models Emma Svensson, Pieter-jan Hoedt, Sepp Hochroiter, Gunter Klambauer [poster] Multi-modal Contrastive Learning with CLOOB Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner, Viet Tran, Fei Tang, Hubert Ramsauer, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, Sepp Hochreiter [poster] Mimicking Iterative Learning with Modern Hopfield Networks for Tabular Data Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, Sepp Hochreiter [poster] A Recurrent Neural Network Model of Travel Direction in Humans Lilian Cheng, Elizabeth R. Chrastil, Jeffrey Krichmar [poster] Automated Deep Lineage Tree Analysis Using Deep Learning with a Bayesian Single Cell Tracking Approach Kristina Ulicna, Giulia Vallardi, Guillaume Charras, Alan R. Lowe [poster] Prostate Cancer Malignancy Detection and Localization From MpMRI Using Auto-Deep Learning: One Step Closer to Clinical Utilization W. ZONG, E. CARVER, S. ZHU , E. SCHAFF, D. CHAPMAN, J. LEE, I. CHETTY, N. WEN [poster] Explaining Structure Activity Relationships Using Locally Faithful Surrogate Models Heta A. Gandhi, Andrew D. White [poster] Affects of Remote Learning on Academic Performance of High School Students Garima Giri, Robert M. Scott, Snigdha Chaturvedi [poster] Fourier-Based Strategies to Explore Ethnic Feature Generation during Visible-to-Thermal Facial Translation (Work-in-progress) Catherine Ordun, Edward Raff, Sanjay Purushotham [poster] Cross-modal contrastive learning of microscopy image and structure-based representations of molecules Ana Sanchez-Fernandez, Elisabeth Rumetshofer, Sepp Hochreiter, Günter Klambauer [poster] CNN-based Emotion Recognition from Multimodal Peripheral Physiological Signals Sowmya Vijayakumar, Ronan Flynn, Peter Corcoran, Niall Murray [poster] Cancer Health Disparity with BERTopic and PyCaret Evaluation Mary Adewunmi, Saksham Kumar Sharma, Nistha Sharma, N Sudha Sharmaa, Bayangmbe Mounmo [poster] Bayesian Optimisation for Active Monitoring of Air Pollution Sigrid Passano Hellan, Christopher G. Lucas and Nigel H. Goddard [poster] Detecting Seen/Unseen Objects with Reducing Response Time for Multimedia Event Processing Asra Aslam [poster] Automated Adaptive Design in Real Time Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth [poster] [Talk] Early Identification of Tuta absoluta in Tomato Plants Using Deep Learning Lilian Mkonyi, Denis Rubanga, Baraka Maiseli, Dina Machuve [poster] Fast and Accurate Method for the Segmentation of Diabetic Foot UlcerImages Rehema Mwawado,Mussa Dida,Baraka Maiseli [poster] Deep Kernel Learning with Personalized Multi-task Gaussian Processes for Longitudinal Prediction in Alzheimer’s Disease Vasiliki Tassopoulou, Fanyang Yu, Christos Davatzikos [poster] Learning to Solve PDE-constrained Inverse Problems with Graph Networks Qingqing Zhao, David Lindell, Gordon Wetzstein [poster] [Talk] Not All Poisons are Created Equal: Robust Training against Data Poisoning Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman [poster] Call for Participation WiML 3rd Un-Workshop @ ICML 2022 [submissions are now closed] The Women in Machine Learning will be organizing the third un-workshop at ICML 2022. The un-workshop is based on the concept of an un-conference, a form of discussion on a pre-selected topic that is primarily driven by participants. Different from the traditional workshop format, the un-workshop’s main focus is topical breakout sessions with short invited talks and casual, informal poster presentations. This is an event format to encourage more participant interaction and we are excited to be able to explore this format in-person for the first time! The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. Students, postdocs and researchers in all areas of Machine Learning who are woman or non-binary are encouraged to submit a one-page proposal to lead a breakout session on a certain research topic. There are many ways to participate, see below! IMPORTANT DATES May 27th, 2022 -- Application Form opens June 17th 19th, 2022 -- Deadline (Anywhere on Earth ) to apply for a breakout session, poster, registration fee funding, facilitating or volunteering June 20th, 2022 -- Notification of acceptance for all of the above (midnight Anywhere on Earth ) July 18th, 2022 -- WiML Un-Workshop Day Various ways of participating in WiML un-workshop Lead a breakout session: submit a proposal to lead a breakout session on a certain research topic. Facilitate a breakout session: assist breakout session leaders by taking notes and encouraging participant interactions and taking attendance. Present a poster: present a poster in a casual, informal setting. Volunteer: help with technical setup and in-event needs. Attend: participate in breakout session discussions. 1. Breakout session proposals: A breakout session is a 1-hour free-form discussion overseen by 1-3 leaders and with assistance from 1-2 facilitators to take notes and encourage participant interactions. We strongly encourage students, postdocs, and researchers who are women or nonbinary in all areas of machine learning to submit a proposal to lead a topical breakout session. A complete proposal consists of a 1 page blind PDF (example here) and the names and bios of leaders submitted separately in the application form . We strongly recommend having at least 2 leaders, with a diverse set of leaders preferred (see selection criteria below). The names of facilitators can also be provided if known at submission time. Otherwise, the organizers will match facilitators to breakout sessions. WiML registration fee funding is prioritized for accepted breakout session leaders who fulfill certain eligibility criteria (see details below) and do not have any other sources of funding. Only one proposal submission per leader is allowed. If there are multiple leaders, only one leader needs to submit the proposal. There are no proceedings. Guidelines for and roles of leaders: Breakout session leaders must be women or nonbinary Point-out key characteristics of your topic and make connections with other topics. Describe the key challenges in this research area on a high-level. Describe the key approaches on a high-level to provide intuition. Highlight possible points of discussion/goals to achieve during the session. Use graphics/imagery and materials e.g. slides as needed Encourage inclusive (rather than unilateral) discussions Leaders should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. Submission instructions for breakout sessions: Proposals must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be minimum 11 point font size and page margins must be minimum 0.75 inches (all sides). Your proposal should stand alone, without linking to a longer paper or supplement. You should provide a brief description of the topics you’d like to discuss, any relevant references, a plan for how you would organize the time (1 hour) allocated for a session, as well as some ideas on how you would encourage discussion and participant interaction during the session. The PDF must not include identifying information, as it will be reviewed blind. In particular, the PDF should not contain information of the leaders or facilitators. Instead, submit their information in the application form . Selection criteria for breakout sessions: The degree to which it is expected that participants will find the topic interesting and valuable. Diversity of leaders and facilitators, including diversity of experience/seniority, affiliation, race, viewpoint and thinking regarding the topic, etc. Plans for encouraging discussion and participant interaction during the session. 2. Facilitators: If you are interested in facilitating a breakout session but have not yet connected with anyone submitting a breakout session proposal, you can indicate your interest in the application form . The role of facilitators is take notes and encourage participant interactions. Organizers will match selected facilitators to breakout sessions. Facilitators should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. Also note that facilitators can be of any gender. 3. Posters: If you wish to present a poster, submit EITHER a short abstract (max 1500 characters) OR a PDF of the poster (only if you have it already). The poster may describe new, previously, concurrently published, or work-in-progress research. Posters in theory, methods, and applications are welcome. Accepted posters will be presented in a casual, informal setting. This setting is very different from formal poster sessions, e.g. at WiML Workshop at NeurIPS. While the exact presentation format is still being determined, we expect to be able to provide spots for everyone to display a physical poster. There are no oral or spotlight presentations, but you will be invited to submit a 5-10 minute video presentation uploaded to a video streaming service. Note that there are no proceedings. Submission instructions for posters: Submitted materials may contain identifying information, as posters for this un-workshop are not reviewed blind. Your submission should stand alone, without linking to a longer paper or supplement. You should convey motivation and give some technical details of the approach used. While we acknowledge that space is limited, some experimental results are likely to improve reviewers’ opinions of your poster. The poster presenter be woman or nonbinary; other authors can be of any gender. The poster presenter does not need to be the first author of the work. Only one poster submission per presenter is allowed. If your poster is not prepared yet, you can submit a one-page abstract, examples of accepted abstracts from previous years can be found here , and advice on writing a one-page abstract can be found here . 4. Volunteering: We are seeking volunteers to help with technical setup and virtual technology testing before the event, as well as help during the event, e.g. letting people into Zoom rooms, etc. We may also need emergency reviewers for breakout session proposals. You can indicate if you can help in any way in corresponding section of the application form . Note: We also encourage you to apply for ICML volunteer and funding opportunities, which are separate and independent of WiML funding. Check the ICML website directly for details. 5. Participation instructions: To participate in ANY of the above roles and/or apply for registration fee funding, please fill in the application form by June 17, 2022. Selected breakout session leaders, facilitators, poster presenters, volunteers, and funding recipients will be notified individually by the dates mentioned above. If you only wish to attend, we still recommend you fill in this form to provide your timezone and topic preferences. All participants are required to abide by the WiML Code of Conduct. 6. Registration fee funding: To apply for funding, you should: (i) be a woman or nonbinary; (ii) be a student, postdoc, or have an equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented geographical regions); (iii) participate in at least one breakout session as a leader, facilitator, or attendee. Due to limited funding, we may not be able to support everyone eligible; however, we hope to support as many eligible applicants as possible. Accepted breakout session leaders who fulfill the above eligibility criteria and do not have any other sources of funding will be prioritized for WiML funding. Other participants are also encouraged to apply. Priority will be given to individuals from underrepresented regions or groups, first-time attendees of ICML or similar conferences, and individuals who would benefit the most from this funding. Further questions? Check out the FAQs or reach us at workshop@wimlworkshop.org PLATINUM SPONSORS Committee ORGANIZERS Paula Gradu General Chair Vinitra Swamy Program Chair Giulia Clerici Breakout Program and Logistics Co-Chair Mozhgan Saeidi Breakout Program and Logistics Co-Chair Noor Sajid Student Program and Volunteers Chair Yina Lin Networking and Mentorship Chair Shweta Khushu Finance and Sponsorship Chair Deeksha Shama Social Event Chair ADVISORY Danielle Belgrave D&I chair Tatjana Chavdarova WiML Board POC SUPER VOLOUNTEERS Archana Vaidheeswaran Women who code FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here ! What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

All events WiML Breakfast @ ICML 2015 Lille, France July 8, 2015 07:00 am — 08:30 am WiML is hosting a breakfast at ICML 2015 in Lille, France. The breakfast is designed to facilitate networking and mentoring for women in machine learning, with specified table topics ranging from advice on career planning to establishing collaborations. Date: Wednesday, 8 July 2015, 7am-8.30am Venue: Room Rotterdam, Lille Grand Palais, 1 Boulevard des Cites-Unies, Lille Registration: https://www.eventbrite.com/e/wiml-icml-breakfast-2015-registration-16531922425 If you see any errors or omissions or have any information to contribute to this page, please contact us at info@wimlworkshop.org SPONSORS Previous Next

All events WiML Un-Workshop @ ICML 2023 Honolulu, Hawai'i July 28, 2023 9:35 am - 10:50 am The 4th WiML Un-Workshop is co-located with ICML on Friday, July 28th, 2023. For more information or to register, please go here. Previous Next