12th Women in Machine Learning Workshop (WiML 2017)

The 12th WiML Workshop is co-located with NIPS in Long Beach, California on Monday, December 4th and Thursday, December 7th, 2017.

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 12th year, the 2017 workshop is co-located with NIPS in Long Beach, California. A History of WiML poster was created to celebrate the 10th workshop, held in 2015 in Montreal, Canada 2015.

Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as breakfast at ICML and AAAI conferences and local meetup events, 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

Jenn Wortman Vaughan

Senior Researcher at Microsoft Research

Raia Hadsell

DeepMind

Tamara Broderick

TT Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT

Hannah Wallach

Senior Researcher at Microsoft Research New York City and an Adjunct Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst

Joelle Pineau

Professor of Computer Science at McGill University where she co-directs the Reasoning and Learning Lab

Nina Mishra

Principal Scientist at Amazon and a visiting scholar at Stanford

Location

Co-located with NIPS in Long Beach, California.

This workshop takes place in Long Beach Convention Center.

Sunday

12:00 – 14:00

Registration Desk Opens

19:00 – 22:30

Pre Workshop Dinner (Optional). Separate Registration Required

Monday

All events are held in Room 104, except for the poster session, which takes place in the Pacific Ballroom

9:00 – 12:00

Registration Desk Opens

11:00 – 11:15

Opening Remarks – Jenn Wortman Vaughan Microsoft Research. Co-Founder of WiML.

11:15 – 11:50

Invited Talk – Tamara Broderick MIT. Bayesian machine learning: Quantifying uncertainty and robustness at scale

11:50 – 12:10

Contributed Talk: Aishwarya Unnikrishnan, Indraprastha Institute of Information Technology Delhi. Towards Communication-Centric Multi-Agent Deep Reinforcement Learning for Guarding a Territory Game.

12:10 – 12:30

Contributed Talk: Peyton Greenside, Stanford University. Graph convolutional networks can encode three-dimensional genome architecture in deep learning models for genomics.

12:30 – 14:00

Lunch + Poster Session

14:00 – 14:35

Invited Talk – Hanna Wallach Microsoft Research. Machine Learning for Social Science

14:35 – 14:50

Coffee Break

14:50 – 15:50

Research and Career Advice Roundtables

15:55 – 16:15

Contributed Talk: Palak Agarwal, WorldQuant. Fairness Aware Recommendations.

16:15 – 16:35

Contributed Talk: Victoria Krakovna, DeepMind. Reinforcement Learning with a Corrupted Reward Channel.

16:35 – 16:45

Closing Remarks

Thursday

All events are held in Room 104, except for the poster session, which takes place in the Pacific Ballroom

10:00 – 14:00

Registration Desk Opens

12:00 – 12:45

Lunch

12:45 – 13:05

Opening Remarks – Raia Hadsell, DeepMind

13:05 – 13:40

Invited Talk – Joelle Pineau Head Facebook AI Research (Montreal Lab)/ Mc Gill. Improving health-care: challenges and opportunities for reinforcement learning

13:40 – 13:55

Contributed Talk: Zhenyi Tang, University of Illinois. Harnessing Adversarial Attacks on Deep Reinforcement Learning for Improving Robustness.

13:55 – 14:10

Contributed Talk: Hoda Heidari, ETH Zurich. A General Framework for Evaluating Callout Mechanisms in Repeated Auctions.

14:10 – 14:20

Coffee Break

14:20 – 15:20

Research and Career Advice Roundtable

15:20 – 15:55

Invited Talk – Nina Mishra Amazon. Time-Critical Machine Learning

15:55 – 16:15

Contributed Talk: Sarah Bouchat, Nothwestern University. Engaging Experts: A Dirichlet Process Approach to Divergent Elicited Priors in Social Science.

16:15 – 16:35

Contributed Talk: Nesreen K Ahmed, Intel Labs. Representation Learning in Large Attributed Graphs.

16:35 – 16:40

Closing Remarks

16:40 – 18:05

Poster Session (Coffee and Snacks Served)

Monday, Dec 4, 12:20 pm to 2:00 pm and Thursday Dec 7, 4:35pm -6:00pm, open to WiML and NIPS attendees

350+ posters covering theory, methodology, and applications of machine learning will be presented across 2 poster sessions.

The list of posters and authors can be found in the program book

Accepted posters (with abstracts). Abstracts listed here are for archival purposes and do not constitute proceedings for this workshop.

Poster size: Up to 48 inches tall and 60 inches wide. We recommend printing in the stand A0 size (33.11 inches by 46.81 inches). You can orient the poster in portrait or landscape as long as it fits within the specified dimensions.

This year we have four categories of mentorship roundtables: Research Roundtables (Tables 1-22), Career Advice Roundtables (Tables 23-42), NIPS Paper Discussion (Tables 43-50), Company Career Tables (Tables 51-63). On Monday 4th December, these tables will take place at 2:50pm – 3:50pm

Table 1: Reinforcement learning I – Katja Hofmann, Microsoft Research

Table 2: Reinforcement learning II – Oriol Vinyals, DeepMind

Table 3: Deep learning I – Yoshua Bengio, MILA – Université de Montréal

Table 4: Deep learning II – Doina Precup, McGill University / Head DeepMind Montreal

Table 5: Bayesian methods I – Meire Fortunato, DeepMind

Table 6: Bayesian methods II –Neil Lawrence, Amazon Research Cambridge

Table 7: Graphical models – Anima Anandkumar, Amazon Web Services/ Caltech

Table 8: Statistical inference, estimation and Optimization – Irina Kukuyeva, Dia&Co

Table 9: Neuroscience – Katharina Volz, Founder OccamzRazor

Table 10: Robotics I – Bonolo Mathibela, IBM Research

Table 11: Black Box vs Open Box ML Approaches – Barbara Engelhardt, Assistant Professor, Princeton

Table 12: Natural language processing – George Dahl, Google Brain

Table 13: Biological Applications – Luisa Cutillo, University Parthenope of Naples

Table 14: Healthcare/Clinical Applications – Marzyeh Ghassemi, MIT/Verily

Table 15: Causal Inference and Counterfactuals – Sara Magliacane, IBM Research

Table 16: Computer Vision – Amy Zhang, Facebook AI Research

Table 17: Fairness, accountability, transparency in ML – Christian Borgs, Microsoft Research

Table 18: Social Sciences Application – Timnit Gebru, Microsoft Research

Table 19: Music Applications – Vidhya Murali, Spotify USA Inc

Table 20: Business Applications – Pallika Kanani, Oracle Labs

Table 21: Industrial Applications in AI/ Commercialising your Research – Jennifer Schumacher, 3M

Table 22: Technical AGI Safety – Victoria Krakovna, DeepMind

Table 23: Creative AI Applications (Art, Music, Design) – Luba Elliott, iambic.a

Table 24: Work-Life Balance (Industry) – Hanna Wallach, Microsoft

Table 25: Work-Life Balance (Academia) – Joelle Pineau, Head Facebook AI Research Montreal/ Professor McGill University

Table 26: Life with Kids / Work-life balance – Caitlin Smallwood

Table 27: Getting a Job (Industry) – Beth Zeranski, Microsoft

Table 28: Getting a Job (Academic) – Yisong Yue, Caltech/ Tamara Broderick MIT

Table 29: Doing a Postdoc – Adriana Romero, Facebook AI Research

Table 30: Choosing between Academia and Industry – Daniel Jiang, University of Pittsburgh

Table 31: Choosing between Academia and Industry – Samy Bengio, Google Brain

Table 32: Doing Research in Industry – Natalia Neverova, Facebook AI Research; Stacey Svetlichnaya, Flickr / Yahoo Research

Table 33: Keeping up with academia while in industry – Nevena Lazic, Google

Table 34: Surviving Graduate School – Lily Hu, Salesforce Research;

Table 35: Establishing Collaborators – Moustapha Cisse, Facebook AI Research

Table 36: Scientific Communication – Chris Bishop, Lab Director Microsoft Research Cambridge

Table 37: Building your Professional Brand – Katherine Gorman, Talking Machines/Collective Next

Table 38: Founding Startups/ Building your Professional Brand – Rachel Thomas, Fast.AI/ University of San Francisco

Table 39: Founding Startups – Philippe Beaudoin, Element AI

Table 40: Early-Stage Start-ups using Machine Learning/Deep Learning – Lisha Li, Amplify Partners

Table 41: Joining Start-ups – Lavanya Tekumalla, Amazon

Table 42: Finding Mentors/ Networking – Lisa Amini, Director IBM Research AI

Table 43: Long-term Career Planning – Jennifer Chayes, Managing Director, Microsoft Research NE & NYC

Table 44: NIPS Paper Discussion: SVCCA: Singular Vector Canonical Correlation Analysis for Deep

Understanding and Improvement – Maithra Raghu, Google Brain and Cornell University

Table 45: NIPS Paper Discussion: Self-supervised Learning of Motion Capture – Katerina Fragkiadaki, Carnegie Mellon University

Table 46: NIPS Paper Discussion: Linear regression without correspondence – Daniel Hsu, Columbia University

Table 47: NIPS Paper Discussion: A-NICE-MC: Adversarial Training for MCMC – Jiaming Song, Stanford University

Table 48: NIPS Paper Discussion: Learning multiple visual domains with residual adapters – Sylvestre-Alvise Rebuffi, University of Oxford

Table 49: NIPS Paper Discussion: Efficient Use of Limited-Memory Resources to Accelerate Linear Learning – Celestine Dünner, IBM Research

Table 50: NIPS Paper Discussion: Variational Inference via χ Upper Bound Minimization – Adji Bousso Dieng, Columbia University

Table 51: NIPS Paper Discussion: Robust Hypothesis Test for Functional Effect with Gaussian Processes – Jeremiah Liu, Harvard University

Table 52: NIPS Paper Discussion: Bayesian Dyadic Trees and Histograms for Regression – Stéphanie van der Pas, Leiden University

Table 52: Careers@ElementAI

Table 53: Careers@Facebook

Table 54: Careers@DeepMind

Table 55: Careers@Capital One

Table 56: Careers@Criteo

Table 57: Careers@Microsoft

Table 58: Careers@Intel

Table 59: Careers@Google

Table 60: Careers@Airbnb

Table 61: Careers@Apple

Table 62: Careers@IBM

Table 63: Careers@NVIDIA

Table 64: Careers@Pandora

This year we have four categories of mentorship roundtables: Research Roundtables (Tables 1-25), Career Advice Roundtables (Tables 26-43), NIPS Paper Discussion (Tables 44-57), Company Career Tables (Tables 51-63). On Thursday 7th December, these tables will take place at 2:20pm – 3:20pm

Table 1: Reinforcement learning I – Raia Hadsell, DeepMind

Table 2: Black Box vs Open Box ML Approaches – Barbara Engelhardt, Assistant Professor, Princeton

Table 3: Deep learning I – Anima Anandkumar, Amazon Web Services/ Caltech

Table 4: Deep learning II – Amy Zhang, Facebook AI Research

Table 5: Bayesian methods I – Chris Bishop, Lab Director Microsoft Research Cambridge

Table 6: Bayesian methods II – Zoubin Ghahramani, University of Cambridge

Table 7: Graphical models – David Blei, Columbia University

Table 8: Generative Models – Ian Goodfellow, Google Brain

Table 9: Technical AGI Safety – Shane Legg, Founder DeepMind

Table 10: Kernel Methods – Corinna Cortes, Head of Google Research

Table 11: Neuroscience – Katharina Volz, Founder OccamzRazor

Table 12: Robotics –

Table 13: Natural language processing – George Dahl, Google Brain

Table 14: Statistical inference and estimation – Timnit Gebru, Microsoft Research

Table 15: Biological Applications – Luisa Cutillo, University Parthenope of Naples

Table 16: Healthcare/Clinical Applications – Marzyeh Ghassemi, MIT/Verily

Table 17: Optimization – Irina Kukuyeva, Dia & Co

Table 18: Causal Inference and Counterfactuals – Sara Magliacane, IBM Research

Table 19: Computer Vision – Natalia Neverova, Facebook AI Research;

Table 20: Fairness, accountability, transparency in ML I – Christian Borgs, Microsoft Research

Table 21: Fairness, accountability, transparency in ML II – Nyalleng Moorosi, Council for Scientific and Industrial Research

Table 22: Learning Theory – Hoda Heidari, ETHZ

Table 23: Social Sciences Application – Lise Getoor, UC Santa Cruz

Table 24: Business Applications – Pallika Kanani, Oracle Labs

Table 25: Industrial Applications in AI/ Commercialising your Research – Jennifer Schumacher, 3M

Table 26: Work-Life Balance (Industry) – Amy Nicholson, Olivia Klose, Microsoft

Table 27: Work-Life Balance (Academia) – Neil Lawrence, Amazon Research Cambridge

Table 28: Life with Kids / Work-life balance – Caitlin Smallwood, Netflix

Table 29: Getting a Job (Industry) – Aleatha Parker-Wood Symantec

Table 30: Getting a Job (Academic) – Yisong Yue, Caltech

Table 31: Doing a Postdoc – Aida Nematzadeh, UC Berkeley

Table 32: Choosing between Academia and Industry – Daniel Hsu, Columbia University

Table 33: Choosing between Academia and Industry – Adriana Romero, Facebook AI Research

Table 34: Doing Research in Industry – Stacey Svetlichnaya Flickr / Yahoo Research

Table 35: Keeping up with academia while in industry – Chew-Yean Yam, Microsoft

Table 36: Surviving Graduate School – Shruthi Kubatur, Nikon Research Corporation of America

Table 37: Establishing Collaborators/ Long-term Career Planning – Jennifer Chayes, Managing Director, Microsoft Research NE & NYC

Table 38: Scientific Communication – Katherine Gorman, Talking Machines/ Collective Next

Table 39: Building your Professional Brand/ Founding Startups – Rachel Thomas, Fast.AI/ University of San Francisco

Table 40: Founding Startups – Philippe Beaudoin, Element AI

Table 41: Early-Stage Start-ups using Machine Learning/Deep Learning – Lisha Li, Amplify Partners

Table 42: Joining Start-ups – Lavanya Tekumalla, Amazon

Table 43: Networking/ Finding Mentors – Muhammad Jamal Afridi, 3M

Table 44:

Table 45: NIPS Paper: A-NICE-MC: Adversarial Training for MCMC – Jiaming Song, Stanford University

Table 46: NIPS Paper: Learning multiple visual domains with residual adapters – Sylvestre-Alvise Rebuffi, University of Oxford

Table 47: NIPS Paper: Variational Inference via χ Upper Bound Minimization – Adji Bousso Dieng, Columbia University

Table 48: NIPS Paper: A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control – Fanny Yang, UC Berkley

Table 49: NIPS Paper: Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin – Ritambhara Singh, University of Virginia

Table 50: NIPS Paper: Style Transfer from Non-parallel Text by Cross-Alignment – Tianxiao Shen, MIT CSAIL

Table 51: NIPS Paper: Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model – Devi Parikh, Georgia Tech / Facebook AI Research

Table 52:

Table 53: NIPS Paper: Inferring Generative Model Structure with Static Analysis – Paroma Varma, Stanford University

Table 54: NIPS Paper: Concrete Dropout (Topic: Bayesian Deep Learning) – Yarin Gal, University of Oxford

Table 55: NIPS Paper: Do Deep Neural Networks Suffer from Crowding? – Anna Volokitin, ETH Zurich

Table 56

Table 57: NIPS Paper: Deanonymization in the Bitcoin P2P Network – Giulia Fanti, Carnegie Mellon University

Table 58: Careers@ElementAI

Table 59: Careers@Facebook

Table 60: Careers@DeepMind

Table 61: Careers@Capital One

Table 62: Careers@Criteo

Table 63: Careers@Microsoft

Table 64: Careers@Intel

Table 65: Careers@Google

Table 66: Careers@Airbnb

Table 67: Careers@Apple

Table 68: Careers@IBM

Table 69: Careers@NVIDIA

Table 70: Careers@Pandora

Call for Participation

The 12th WiML Workshop is co-located with NIPS in Long Beach, California on Monday, December 4th and Thursday, December 7th, 2017.

The workshop is a two-day event with invited speakers, oral presentations, and posters. The event brings together faculty, graduate students, and research scientists for an opportunity to connect and exchange ideas. There will be a panel discussion and 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. While all presenters will identify primarily as female, all genders are invited to attend. This is a technical workshop with exciting technical talks.

Important Dates

September 12th, 2017 11:59 pm PST – Extended Abstract submission deadline
September 8th, 2017 11:59 pm PST – Abstract submission deadline
October 16th, 2017 – Notification of abstract acceptance
November 1st, 2017 – Travel grant application deadline
November 14th, 2017 – Registration Deadline
December 4th, 2017 – Workshop Day 1
December 7th, 2017 – Workshop Day 2

Submission Instructions

We strongly encourage primarily female-identifying students, post-docs and researchers in all areas of machine learning to submit an abstract describing new, previously, or concurrently published research. We welcome abstract submissions, in theory, methodology, as well as applications. Abstracts may describe completed research or work-in-progress. While the presenting author need not be the first author of the work, we encourage authors to highlight the contribution of female authors — particularly the presenting author — in the abstract.

Authors of accepted abstracts will be asked to present their work in a poster session. A few authors will be selected to give 15 minute oral presentations. Submissions will be peer-reviewed in a double-blind setting. Authors are encouraged to sign up to review for WiML, with a sign-up option available upon submission. Student and post-doc authors who review for WiML will be eligible for travel awards.

Submission page: https://cmt3.research.microsoft.com/WiML2017

Style guidelines:

Abstracts must not include identifying information
Abstracts must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be 11 points in size.
Do not include any supplementary files with your submission.

Content guidelines:

Your abstract 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 appreciate that space is limited, some experimental results are likely to improve reviewers’ opinions of your paper.

Acceptance criteria:

All accepted papers must be presented by a primarily female-identifying author. Abstracts will be reviewed by at least two reviewers plus an area chair, who will use the following criteria:

Is this paper appropriate for WiML? I.e. Does it describe original research in Machine Learning or related fields?
Does the abstract describe work that is novel and/or an interesting application?
Does the abstract adequately convey the material that will be presented?

Examples of accepted abstracts from previous years. Due to the volume of submissions anticipated, we are unable to review any submitted materials besides the requested abstract.

Travel Scholarships

Registration is free. Travel Awards are available for presenting authors only. To qualify, the author must be a student or post-doc, their abstract must be accepted, and they must volunteer to serve as a reviewer for WiML. The amount of the travel award varies by the author’s geographical location and the total amount of funding WiML receives from our sponsors. In the past awards ranging from $300-$900 have been granted.

Organizers