• Negar Rostamzadeh
  • October 28, 2016

Welcome to WiML 2017! A printable version of the information below, will be soon open. Our poster session (Mon Dec 4, 12.20-2.00pm, and Thursday Dec 7, 12:20 pm- 2:35 pm) is open to WiML and NIPS attendees.

12.00 – 14.00 – Registration desk open.


11:00 – 11:05 – Opening Remarks

11:05 – 11:40 – Invited Talk – Tamara Broderick

11:40 – 12:00 – Contributed Talk: Aishwarya Unnikrishnan, Indraprastha Institute of Information Technology Delhi. Towards Communication-Centric Multi-Agent Deep Reinforcement Learning for Guarding a Territory Game. [Abstract]

12:00 – 12:20 – Contributed Talk: Peyton Greenside, Stanford University. Graph convolutional networks can encode three-dimensional genome architecture in deep learning models for genomics. [Abstract]

12:20 – 14:00 – Lunch + Poster Session 

14:00 – 14:35 – Invited Talk – Hanna Wallach

14:35 – 14:50 – Coffee Break

14:50 – 15:50 – Career and Advice Roundtable

15:55 – 16:15 – Contributed Talk: Palak Agarwal, WorldQuant. Fairness Aware Recommendations. [Abstract]

16:15 – 16:35 – Contributed Talk: Victoria Krakovna, DeepMind. Reinforcement Learning with a Corrupted Reward Channel. [Abstract]

16:35 – 16:45 – Closing Remarks


11:45 – 12:45 – Lunch

12:45 – 13:05 – Opening Remarks

13:05 – 13:40 – Invited Talk – Joelle Pineau

13:40 – 13:55 – Contributed Talk: Zhenyi Tang, University of Illinois. Harnessing Adversarial Attacks on Deep Reinforcement Learning for Improving Robustness. [Abstract]

13:55 – 14:10 – Contributed Talk: Hoda Heidari, ETH Zurich. A General Framework for Evaluating Callout Mechanisms in Repeated Auctions. [Abstract]

14:10 – 14:20 – Coffee Break

14:20 – 15:20 – Career and Advice Roundtable

15:20 – 15:55 – Invited Talk – Nina Mishra

15:55 – 16:15 – Contributed Talk: Sarah Bouchat, Nothwestern University. Engaging Experts: A Dirichlet Process Approach to Divergent Elicited Priors in Social Science. [Abstract]

16:15 – 16:35 – Contributed Talk: Nesreen K Ahmed, Intel Labs. Representation Learning in Large Attributed Graphs. [Abstract]

16:35 – 18:00 – Poster Session (Coffee and Snacks Served)

18:00 – 18:05 – Closing Remarks


Invited Talks

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Abstract:  Reinforcement learning offers a powerful paradigm for automatically discovering and optimizing sequential treatments for chronic and life-threatening diseases. In particular, we will focus on how data collected in multi-stage sequential trials can be used to automatically generate treatment strategies that are tailored to patient characteristics and time-dependent outcomes. We will also examine promising methods to improve the efficiency of clinical trials through adaptation. Examples will be drawn from ongoing research projects on developing new treatment strategies for epilepsy and cancer.

Bio: Joelle Pineau is a professor of  Computer Science at McGill University where she co-directs the Reasoning and Learning Lab. This year she has been appointed the head Facebook AI Research’s new research lab in Montreal. Dr. Pineau’s research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Artificial Intelligence Research and the Journal of Machine Learning Research and is currently President of the International Machine Learning Society. She is a Senior Fellow of the Canadian Institute for Advanced Research and in 2016 was named a member of the College of New Scholars, Artists and Scientists by the Royal Society of Canada.

Bio: Hannah Wallach is a co-founded the annual Women in Machine Learning Workshop in 2005. She is a senior researcher at Microsoft Research New York City and an adjunct associate professor in the College of Computer Science at the University of Massachusetts Amherst. She has a BA in computer science from the University of Cambridge, an MS in cognitive science from the University of Edinburgh, and a PhD in machine learning from the University of Cambridge. Her research is in the interdisciplinary field of computational social science, developing machine learning and natural language processing methods for analyzing the structure, content, and dynamics of social processes. Her research has had broad impact in both computer science and the social sciences. In 2010, her work on infinitely deep belief networks won the best paper award at the Artificial Intelligence and Statistics conference; in 2014, she was named one of Glamour magazine’s “35 Women Under 35 Who Are Changing the Tech Industry”; in 2015, she was elected to the International Machine Learning Society’s Board of Trustees; and in 2016, she was named co-winner of the Borg Early Career Award. She recently ran an eight-week-long data science summer school intended to increase diversity in computer science. She is the program co-chair of NIPS 2017.

BioTamara Broderick is the ITT Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT. Her work is in the areas of statistics and machine learning. Before joining MIT, she completed her PhD in Statistics at UC Berkeley in 2014 with Professor Michael I. Jordan. Her research interests are in Bayesian inference and graphical models—with an emphasis on scalable, nonparametric, and unsupervised learning. She has been awarded a Google Faculty Research Award, the ISBA Lifetime Members Junior Researcher Award, the Savage Award (for an outstanding doctoral dissertation in Bayesian theory and methods), the Evelyn Fix Memorial Medal and Citation (for the Ph.D. student on the Berkeley campus showing the greatest promise in statistical research), the Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton senior with the highest academic average).

BioNina Mishra is a Principal Scientist at Amazon and a visiting scholar at Stanford. She has many years of experience leading projects in industry at Amazon, Microsoft Research and HP Labs, as well as academia as Associate Professor at the University of Virginia and Acting Faculty at Stanford University. Her research interests are in data science, data mining, web search, machine learning and privacy. The projects that she pursues encompass the design and evaluation of new data mining algorithms on real, colossal-sized datasets. She has authored ~50 publications in top venues including: Web Search: WWW, WSDM, SIGIR; Machine Learning: ICML, NIPS, AAAI, COLT; Databases: VLDB, PODS; Cryptography: CRYPTO, EUROCRYPT; Theory: FOCS and SODA. Her research publications received external recognition: best paper award nomination, algorithm in Wikipedia and taught in graduate courses around the world. Also, her research has had product implications at Microsoft, specifically in the Bing search engine, and was featured in external press coverage including New Scientist, ACM TechNews, IEEE Computing Now, Search Engine Land and Microsoft Research. She’s been granted 14 patent applications with a dozen more still in the application stage. Her service to the community includes: serving on journal editorial boards Machine Learning, Journal of Privacy and Confidentiality, IEEE Transactions on Knowledge and Data Engineering and IEEE Intelligent Systems; chairing the premier machine learning conference ICML in 2003, as well as numerous program committees for web search, data mining and machine learning conferences.


Research Roundtables

Details of mentoring roundtables willl be soon announced!


WiML 2017 Poster Session

Monday, Dec 4, 12:20 pm to 2:00 pm, open to WiML and NIPS attendees

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

Accepted posters

Information for poster presenters:

The poster assignments for each round are located here. The number in the document is your CMT submission number.

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.  

Copyright © Women in Machine Learning 2017