Craig Boutilier

Craig Boutilier

Craig Boutilier is Principal Scientist at Google. He works on various aspects of decision making under uncertainty, with a current focus on sequential decision models: reinforcement learning, Markov decision processes, temporal models, etc.

Positions and Appointments:
He was a Professor in the Department of Computer Science at the University of Toronto (on leave) and Canada Research Chair in Adaptive Decision Making for Intelligent Systems. He received his Ph.D. in Computer Science from the University of Toronto in 1992, and worked as an Assistant and Associate Professor at the University of British Columbia from 1991 until his return to Toronto in 1999. He served as Chair of the Department of Computer Science at Toronto from 2004-2010. He was co-founder (with Tyler Lu) of Granata Decision Systems from 2012-2015, until his move to Google in 2015.

Boutilier was a consulting professor at Stanford University from 1998-2000, an adjunct professor at the University of British Columbia from 1999-2010, and a visiting professor at Brown University in 1998, at the University of Toronto in 1997-98, at Carnegie Mellon University in 2008-09, and at Université Paris-Dauphine (Paris IX) in the spring of 2011. He served on the Technical Advisory Board of CombineNet, Inc. from 2001 to 2010.

Research:
Boutilier's current research efforts focus on various aspects of decision making under uncertainty, including the use of generative models and LLMs, in areas such as: recommender systems, preference modeling and elicitation, mechanism design, game theory and multiagent decision processes, economic models, social choice, computational advertising, Markov decision processes, reinforcement learning and probabilistic inference. His research interests have spanned a wide range of topics, from knowledge representation, belief revision, default reasoning, and philosophical logic, to probabilistic reasoning, decision making under uncertainty, multiagent systems, and machine learning.

Research & Academic Service:
Boutilier is a past Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR). He was a past Associate Editor with the ACM Transactions on Economics and Computation (TEAC), the Journal of Artificial Intelligence Research (JAIR), the Journal of Machine Learning Research (JMLR), and Autonomous Agents and Multiagent Systems (AAMAS); and he has sat on the editorial/advisory boards of several other journals. Boutilier has organized several international conferences and workshops, including his work as Program Chair of the Twenty-first International Joint Conference on Artificial Intelligence (IJCAI-09) and Program Chair of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-2000). He has also served on the conference program committees of roughly 75 leading international conferences.

He will serve as Conference Chair of the Thirty-seventh International Joint Conference on Artificial Intelligence (IJCAI-28).

Awards and Honors:
Boutilier is a Fellow of the Royal Society of Canada (RSC), the Association for Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI). He was the recipient of the 2018 ACM/SIGAI Autonomous Agents Research Award, He was awarded a Tier I Canada Research Chair, an Isaac Walton Killam Research Fellowship, and an IBM Faculty Award. He received the Killam Teaching Award from the University of British Columbia in 1997. He has also received a number of Best Paper awards including:

Authored Publications
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DynaMITE-RL: A Dynamic Model for Improved Temporal Meta Reinforcement Learning
Anthony Liang
Erdem Biyik
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS-24), Vancouver (2024)
Model-Free Preference Elicitation
Carlos Martin
Tuomas Sandholm
Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI-24), Jeju, South Korea (2024), pp. 3493-3503
Embedding-Aligned Language Models
Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS-24), Vancouver (2024)
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Martin Mladenov
Proceedings of the 38th Annual AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver (2024) (to appear)
Factual and Personalized Recommendation Language Modeling with Reinforcement Learning
Jihwan Jeong
Mohammad Ghavamzadeh
Proceedings of the First Conference on Language Modeling (COLM-24), Philadelphia (2024)
Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
Martin Mladenov
James Pine
Hubert Pham
Shane Li
Xujian Liang
Anton Polishko
Li Yang
Ben Scheetz
Proceedings of he 47th International ACM/SIGIR Conference on Research and Development in Information Retrieval (SIGIR-24), Washington, DC (2024), pp. 2925-2929
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Christina Göpfert
Alex Haig
Ivan Vendrov
Tyler Lu
Hubert Pham
Mohammad Ghavamzadeh
ACM Transactions on Recommender Systems (2024)
Building Human Values into Recommender Systems: An Interdisciplinary Synthesis and Open Problems
Jonathan Stray
Alon Halevy
Parisa Assar
Dylan Hadfield-menell
Amar Ashar
Chloe Bakalar
Lex Beattie
Michael Ekstrand
Claire Leibowicz
Connie Moon Sehat
Sara Johansen
Lianne Kerlin
David Vickrey
Spandana Singh
Sanne Vrijenhoek
Amy Zhang
Mckane Andrus
Natali Helberger
Polina Proutskova
Tanushree Mitra
Nina Vasan
ACM Transactions on Recommender Systems (2023)