FYI. Please forward to possibly interested people.
-------- Forwarded Message -------- Subject: Craig Boutilier @ Challenges and Opportunities in Multiagent RL Date: Thu, 4 Feb 2021 14:08:43 +0100 From: Frans Oliehoek fa.oliehoek@gmail.com To: Frans Oliehoek - EWI F.A.Oliehoek@tudelft.nl CC: c.amato@northeastern.edu, garnelo@google.com, f.a.oliehoek@tudelft.nl, somidshafiei@google.com, karltuyls@google.com
Dear all,
After a fantastic inaugural presentation by Michael Bowling, we are excited to announce the next speaker in our virtual seminar series on the Challenges and Opportunities for Multiagent Reinforcement Learning (COMARL):
Speaker: Craig Boutilier, Google Research
Title: Maximizing User Social Welfare in Recommender Ecosystems
(abstract and bio can be found below)
Date: Thursday February 11th, 2021
Time: 17:00 CET / 16:00 UTC / 08:00 PST
Location: via google meet or youtube
For detailed instructions on how to join, please see here:
https://sites.google.com/view/comarl-seminars/how-to-attend
For additional information, please see our:
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Website https://sites.google.com/view/comarl-seminars(includes schedule, instructions on how to join, etc.)
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Twitter account (for speaker announcements and more!):@ComarlSeminars https://twitter.com/ComarlSeminars
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Google Groups (to receive invitations): comarlseminars@googlegroups.com mailto:comarlseminars@googlegroups.com
We look forward to seeing you there!
Best regards from the organizers,
Chris Amato (Northeastern University),
Marta Garnelo (DeepMind),
Frans Oliehoek (TU Delft),
Shayegan Omidshafiei (DeepMind),
Karl Tuyls (DeepMind)
Speaker:
Craig Boutilier
Google Research,
Mountain View, CA, USA
Title:
Maximizing User Social Welfare in Recommender Ecosystems
Abstract:
An important goal for recommender systems is to make recommendations that maximize some form of user utility over (ideally, extended periods of) time. While reinforcement learning has started to find limited application in recommendation settings, for the most part, practical recommender systems remain "myopic" (i.e., focused on immediate user responses). Moreover, they are "local" in the sense that they rarely consider the impact that a recommendation made to one user may have on the ability to serve other users. These latter "ecosystem effects" play a critical role in optimizing long-term user utility. In this talk, I describe some recent work we have been doing to optimize user utility and social welfare using reinforcement learning and equilibrium modeling of the recommender ecosystem; draw connections between these models and notions such as fairness and incentive design; and outline some future challenges for the community.
Bio:
Craig Boutilier is a Principal Scientist at Google. He received his Ph.D. in Computer Science from U. Toronto (1992), and has held positions at U. British Columbia and U. Toronto (where he served as Chair of the Dept. of Computer Science). He co-founded Granata Decision Systems, served as a technical advisor for CombineNet, Inc., and has held consulting/visiting professor appointments at Stanford, Brown, CMU and Paris-Dauphine.
Boutilier's current research focuses on various aspects of decision making under uncertainty, including: recommender systems; user modeling; MDPs, reinforcement learning and bandits; preference modeling and elicitation; mechanism design, game theory and multi-agent decision processes; and related areas. Past research has also dealt with: knowledge representation, belief revision, default reasoning and modal logic; probabilistic reasoning and graphical models; multi-agent systems; and social choice.
Boutilier served as Program Chair for IJCAI-09 and UAI-2000, and as Editor-in-Chief of the Journal of AI Research (JAIR). He is a Fellow of the Royal Society of Canada (FRSC), the Association for Computing Machinery (ACM) and the Association for the Advancement of Artificial Intelligence (AAAI). He also received the 2018 ACM/SIGAI Autonomous Agents Research Award.