11th Women in Machine Learning Workshop (WiML 2016)

Monday, December 5, 2016

Co-Located with NIPS in Barcelona, Spain

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 11th year, the 2016 workshop is co-located with NIPS in Barcelona, Spain on December 5, 2016. 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

Jennifer Chayes

Microsoft Research

Maya Gupta

Google Research

Anima Anandkumar

Amazon / UC Irvine

Suchi Saria

John Hopkins Univ

Location

The workshop takes place in Centre de Convencions Internacional Barcelona, located at Plaça de Willy Brandt, 11-14, 08019 Barcelona, Spain.

Sunday, Dec 4

12.00 – 14.00

Registration desk open. Entrance Hall (enter from Entrance C)

14.00 – 19.00

Workshop on Effective Communication by Katherine Gorman of Talking Machines and Amazon (Optional). Invitation-only, RSVP required

16.00 – 18.00

Amazon Panel & Networking (Optional). Invitation-only, RSVP required

17.00 – 19.00

Facebook Lean-In Circles (Optional). Invitation-only, RSVP required

19.15 – 22.00

WiML Dinner (Optional). Separate registration required. Dedicated to Amazon

22.00 – 23.30

OpenAI Happy Hour (Optional). Invitation-only, RSVP required

Monday, Dec 5

All events are held in Rooms 111 and 112, level P1, CCIB except for the poster session, which takes place in Area 5+6+7+8, level P0.

07.00 – 08.00

Registration and Breakfast. Dedicated to Microsoft and OpenAI. Registration desk at Entrance Hall (enter from Entrance C); Breakfast in Rooms 111 and 112, level P1

08.00 – 08.05

Opening Remarks

08.05 – 08.40

Invited Talk: Maya Gupta, Google Research. Designing Algorithms for Practical Machine Learning. [Abstract] [Video]

08.40 – 08.55

Contributed Talk: Maithra Raghu, Cornell Univ / Google Brain. On the Expressive Power of Deep Neural Networks. [Abstract] [Video]

08.55 – 09.10

Contributed Talk: Sara Magliacane, VU Univ Amsterdam. Ancestral Causal Inference. [Abstract] [Video] [Slides]

09.10 – 09.15

Break

09.15 – 10.15

Research Roundtables (Coffee served until 9.40am). Dedicated to Apple and Facebook

10.15 – 10.50

Invited Talk: Suchi Saria, John Hopkins Univ. Towards a Reasoning Engine for Individualizing Healthcare. [Abstract] [Video]

10.50 – 11.05

Contributed Talk: Madalina Fiterau, Stanford Univ. Learning Representations from Time Series Data through Contextualized LSTMs. [Abstract] [Video]

11.05 – 11.10

Break

11.10 – 11.25

Contributed Talk: Konstantina Christakopoulou, Univ Minnesota. Towards Conversational Recommender Systems. [Abstract] [Video] [Slides]

11.25 – 12.00

Invited Talk: Anima Anandkumar, Amazon / UC Irvine. Large-Scale Machine Learning through Spectral Methods: Theory & Practice. [Abstract] [Video] [Slides]

12.00 – 13.00

Career & Advice Roundtables

13.00 – 13.30

Lunch and Poster Setup. Dedicated to DeepMind and Google

13.30 – 15.30

Poster Session (Coffee served until 2pm). Open to WiML and NIPS attendees. Dedicated to our Silver Sponsors: Capital One, D.E. Shaw, Intel, Twitter. Area 5+6+7+8, level P0; Round 1: 1.40pm – 2.30pm; Round 2:

2.30pm – 3.20pm; Poster Removal: 3.20pm – 3.30pm

15.30 – 15.45

Raffle and WiML Updates: Tamara Broderick, MIT and Sinead Williamson, UT Austin. [Video]

15.45 – 16.00

Contributed Talk: Amy Zhang, Facebook. Using Convolutional Neural Networks to Estimate Population Density from High Resolution Satellite Images. [Abstract] [Video]

16.00 – 16.35

Invited Talk: Jennifer Chayes, Microsoft Research. Graphons and Machine Learning: Estimation of Sparse Massive Networks. [Abstract] [Video]

16.35 – 16.40

Closing Remarks

NIPS Main Conference (NIPS registration required)

17.00

NIPS Opening Remarks. Area 1 + 2, level P0

WiML 2016 Poster Session

Monday, Dec 5, 1.30pm to 3:30pm, Area 5+6+7+8, level P0, open to WiML and NIPS attendees

200+ posters covering theory, methodology, and applications of machine learning will be presented in 2 rounds.
Accepted posters

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

Information for poster presenters:

Posters for both rounds should be setup 1-1.40pm and removed 3.20-3.30pm. Each poster board is shared by 2-3 presenters. Please check the program book for your round number and poster number. Look for that number in the poster room with ‘W’ appended to the front, e.g. W1, W2, etc.

Poster size: up to 37.9 inches width and 35.8 inches height (or 96.3 cm x 91.0 cm), portrait or landscape.

Research Roundtables

9.15 am - 10.15 am. Coffee served until 9.40 am.

Table 1: Deep learning I – Katja Hofmann, Microsoft Research, Oriol Vinyals, DeepMind

Table 2: Deep learning II – Junli Gu, Tesla, Sergio Guadarrama, Google Research, Niv Sundaram, Intel

Table 3: Reinforcement learning – Emma Brunskill, Carnegie Mellon / Stanford, Yisong Yue, Caltech

Table 4: Bayesian methods I – Barbara Engelhardt, Princeton, Lamiae Azizi, University of Sydney

Table 5: Bayesian methods II – Ferenc Huszar, Twitter / Magic Pony

Table 6: Graphical models – Margaret Mitchell, Google Research, Danielle Belgrave, Imperial College London

Table 7: Learning theory – Cynthia Rush, Columbia University, Corinna Cortes, Google Research

Table 8: Statistical inference and estimation – Katherine M. Kinnaird, Brown University, Alessandra Tosi, Mind Foundry, Oxford

Table 9: Optimization – Anima Anandkumar, Amazon / UC Irvine, Puja Das, Apple

Table 10: Neuroscience – Irina Higgins, DeepMind, Jascha Sohl-Dickstein, Google Brain

Table 11: Robotics – Raia Hadsell, DeepMind, Julie Bernauer, NVIDIA

Table 12: Natural language processing I – Catherine Breslin, Amazon, Olivia Buzek, IBM Watson

Table 13: Natural language processing II – Pallika Kanani, Oracle Labs, Ana Peleteiro Ramallo, Zalando, Aline Villavicencio, Federal University of Rio Grande do Sul, Brazil

Table 14: Healthcare/biology applications – Tania Cerquitelli, Politecnico di Torino, Jennifer Healey, Intel

Table 15: Music applications – Luba Elliott, iambicai, Kat Ellis, Amazon Music, Emilia Gomez, Universitat Pompeu Fabra, Barcelona

Table 16: Social science applications – Allison Chaney, Princeton University, Isabel Valera, Max Planck Institute for Software Systems

Table 17: Fairness, accountability, transparency in machine learning – Sarah Bird, Microsoft, Ekaterina Kochmar, University of Cambridge

Table 18: Computational sustainability – Erin LeDell, H2O.ai, Jennifer Dy, Northeastern University

Table 19: Computer vision – Judy Hoffman, Stanford University, Manohar Paluri, Facebook

Table 20: Human-in-the-Loop Learning – Been Kim, Allen Institute for AI / Univ of Washington, Saleema Amershi, Microsoft Research

Table 1: Machine Learning @Amazon: Jumpstarting your career in industry – Anima Anandkumar, Catherine Breslin, Enrica Maria Fillipi

Table 2: Careers@Apple – Meriko Borogove, Anh Nguyen

Table 3: Machine Learning @DeepMind: Research in industry vs. academia – Nando De Freitas, Viorica Patraucean, Kimberly Stachenfeld

Table 4: Machine Learning @Facebook: Sponsorship vs. Mentorship Throughout Your Career – Angela Fan, Amy Zhang, Christy Sauper, Natalia Neverova, Manohar Paluri

Table 5: Machine Learning @Google: Industrial Research and Academic Impact – Corinna Cortes, Google

Table 6: Machine Learning and Deep Learning @Microsoft – Christopher Bishop, Mir Rosenberg, Anusua Trivedi

Table 7: Delivering phenomenal customer experiences with Machine Learning @Capital One – Jennifer Hill, Marcie Apelt

Table 8: Networking I – Olivia Buzek, IBM Watson, Jennifer Healey, Intel

Table 9: Networking II – Pallika Kanani, Oracle Labs, Been Kim, Allen Institute for AI / Univ of Washington

Table 10: Work/Life Balance (academia) – Namrata Vaswani, Iowa State University, Beka Steorts, Duke University

Table 11: Work/Life Balance (industry) I – Yuanyuan Pao, Lyft, Antonio Penta, United Technologies Research Centre, Ireland

Table 12: Work/Life Balance (industry) II – Kat Ellis, Amazon Music, Puja Das, Apple

Table 13: Choosing between academia/industry I – Katherine M. Kinnaird, Brown University, Jascha Sohl-Dickstein, Google Brain

Table 14: Choosing between academia/industry II – Sarah Bird, Microsoft, Oriol Vinyals, DeepMind

Table 15: Life with Kids – Jenn Wortman Vaughan, Microsoft Research, Julie Bernauer, NVIDIA

Table 16: Getting a job (academia) I – Jennifer Chayes, Microsoft Research, Yisong Yue, Caltech

Table 17: Getting a job (academia) II – Tamara Broderick, MIT, Cynthia Rush, Columbia University

Table 18: Getting a job (industry) I – Anne-Marie Tousch, Criteo, Sergio Guadarrama, Google Research

Table 19: Getting a job (industry) II – Margaret Mitchell, Google Research, Erin LeDell, H2O.ai

Table 20: Doing a postdoc – Cristina Savin, IST Austria / NYU, Judy Hoffman, Stanford University

Table 21: Doing research in industry – Junli Gu, Tesla, Samy Bengio, Google Brain

Table 22: Keeping up with academia while in industry – Irina Higgins, DeepMind, Alessandra Tosi, Mind Foundry, Oxford

Table 23: Surviving graduate school – Allison Chaney, Princeton University, Viktoriya Krakovna, DeepMind

Table 24: Seeking funding: fellowships and grants – Aline Villavicencio, Federal University of Rio Grande do Sul, Brazil, Danielle Belgrave, Imperial College London

Table 25: Establishing collaborations – Barbara Engelhardt, Princeton University, Ekaterina Kochmar, University of Cambridge

Table 26: Joining startups – Alyssa Frazee, Stripe, Ferenc Huszar, Twitter / Magic Pony

Table 27: Scientific communication – Katherine Gorman, Talking Machines, Ana Peleteiro Ramallo, Zalando

Table 28: Building your professional brand – Luba Elliott, iambicai, Lamiae Azizi, The University of Sydney

Table 29: Commercializing your research – Katherine Boyle, General Catalyst, Zehan Wang, Twitter / Magic Pony

Table 30: Long-term career planning – Inmar Givoni, Kindred.ai, Jennifer Dy, Northeastern University

Career & Advice Roundtables

12 pm - 1 pm