All talks held in Pacific Time. **Schedule subject to change**

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Coffee and Check-in
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Opening Remarks
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Talk By
Constantinos Daskalakis (Massachusetts Institute of Technology)
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Break
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Independent and Decentralized Learning in Markov Potential Games
Manxi Wu (Cornell University)
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Breaking the Curse of Multiagency in Multi-Agent RL: Decentralized Function Approximation and Faster Convergence
Yu Bai (Salesforce Research)
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Lunch
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Reinforcement Learning Goes for Gold: How Policy Gradient Methods Converge to Winning Strategies in Stochastic Games
Emmanouil Vasileios Vlatakis Gkaragkounis (UC Berkeley)
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Memory Bounds for Continual Learning
Binghui Peng (Columbia University)
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Break
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Collaboration Time
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Reception
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Coffee and Check-in
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New perspectives on dynamical systems
Christos Papadimitriou (Columbia University)
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Break
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Statistical Contract Theory
Michael Jordan (University of California, Berkeley)
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Repeated multi-unit auctions for carbon
Simina Branzei (Purdue University)
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Lunch
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Collaboration Time
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Break
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Collaboration Time
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Coffee and Check-in
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The statistical complexity of interactive decision making: A minimax optimization perspective
Dylan Foster (Microsoft Research)
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Break
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On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring.
Noah Golowich (MIT & Google)
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Lunch
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Optimizing in the Presence of Strategic Behavior
Eric Mazumdar (California Institute of Technology)
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Break
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Gaming the Learning
Amin Karbasi (Yale University)
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Coffee and Check-in
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Hybrid RL
Jason Lee (Princeton University)
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Break
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Making Minimax Optimization Adaptive and Reproducible
Niao He (ETH Zürich)
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Lunch
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Higher-Order Uncoupled Dynamics Do Not Lead to Nash Equilibrium - Except When They Do
Jeff S. Shamma (University of Illinois Urbana-Champaign)
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Break
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Collaboration Time