All talks are in Pacific Time.

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Coffee and Check-In
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Opening Remarks
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Last-Iterate Convergence in Constrained Min-Max Optimization: SOS to the Rescue
Yang Cai (Yale University)
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Break
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Optimistic Gradient Methods for Multi-Agent Learning
Kaiqing Zhang (MIT)
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Break
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Fast Rates for No-Regret Learning in General Games
Noah Golowich (MIT)
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Lunch
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Offline Reinforcement Learning With Only Realizability
Jason Lee (Princeton)
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Break
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The Variational Method of Moments
Nathan Kallus (Cornell University)
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Reception
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Coffee and Check-In
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Min-max Optimization: From Complexity to Algorithms
Emmanouil Zampetakis (UC Berkeley)
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Break
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Min-Max Optimization from a Dynamical Systems Viewpoint
Panayotis Mertikopoulos (CNRS)
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Break
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Smooth Nonconvex Min-Max Optimization
Meisam Razaviyayn (University of Southern California)
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Lunch
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Studying Failure Modes of Deep Learning and Ways To Fix Them
Soheil Feizi (University of Maryland, College Park)
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Break
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Is Minimax Optimization the Right Framework to Understand Learning in Minimax Games?
Gauthier Gidel (Université de Montréal)
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Break
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Beyond Adversarial Approaches on Distributional Robustness
Qi Lei (Princeton)
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Coffee and Check-In
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Computationally Efficient Alternatives to Nonconvex-Nonconcave Min-Max Optimization
Nisheeth Vishnoi (Yale University)
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Break
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Halpern Iteration and Equilibria Problems
Jelena Diakonikolas (University of Wisconsin-Madison)
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Break
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Zeroth-Order Methods for Convex-Concave Minmax Problems: Learning from Strategically Generated Data
Chinmay Maheshwari (UC Berkeley)
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Lunch
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Are Single-Loop Algorithms Sufficient for Unbalanced Minimax Optimization？
Niao He (ETH Zürich)
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Break
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Policy Gradient: Optimal Estimation, Convergence, and Generalization beyond Cumulative Rewards
Mengdi Wang (Princeton University)
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Break
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Of Moments and Matching: Trade-offs and Treatments in Imitation Learning
Steven Wu (Carnegie Mellon University)
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Coffee and Check-In
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Learning Uninformative Representations
Richard Zemel (Columbia University)
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Break
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Generalized Energy-Based Models
Arthur Gretton (UCL)
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Break
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Rebel: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
Noam Brown (Facebook AI Research)
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Lunch
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Online Adversarial Multicalibration And (Multi)Calibeating
Aaron Roth (University of Pennsylvania)
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Break
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Adversarial Machine Learning and Instrumental Variables for Flexible Causal Modeling
Vasilis Syrgkanis (Microsoft Research)