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Coffee and Check-In
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Opening Remarks
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The Limits Of Regularized Learning In Games
Panayotis Mertikopoulos (French National Center for Scientific Research (CNRS)
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Break
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What Does It Take To Be a Good Fisherman?
Costantinos Daskalakis (MIT)
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Lunch
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Identification Analysis In Models With Set Valued Predictions
Francesca Mollinari (Cornell University)
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Break
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Mechanisms To Procure Information Without Verification
Grant Schoenebeck (University of Michigan)
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Reception
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Coffee and Check-In
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Forecast Hedging, Calibration, And Game Equilibria
Sergiu Hart (Hebrew University of Jerusalem)
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Break
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Optimal No-Regret Learning in General Games via Clairvoyant MWU
Georgios Piliouras (Singapore University of Technology and Design)
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Lunch
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Predicting Cooperation Rates With A Learning Model
Drew Fudenberg (MIT)
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Break
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Beyond Open Loop Algorithm Design: Learning from Decision-Dependent Data
Lillian Ratliff (University of Washington)
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Coffee and Check-In
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Aggregating Conflicting Forecasts
Tim Roughgarden (Columbia University)
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Break
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Experimental Evidence of Individual Human Learning in Games
Colin F. Camerer (Caltech)
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Lunch
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In Praise of Game Dynamics
Christos Papadimitriou (Columbia University)
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Break
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Analysis of the Alternating Mirror Descent for Constrained Min-Max Games
Andre Wibisono (Yale University)
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Coffee and Check-In
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The Transfer Performance of Economic Models
Annie Liang (Northwestern University)
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Break
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Learning, Incentivizing Improvement, and Fairness in the Presence of Strategic Agents
Avrim Blum (Toyota Technological Institute at Chicago)
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Lunch
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The Challenge of Understanding What Users Want: Inconsistent Preferences and Engagement Optimization
Jon Kleinberg (Cornell University)
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Break
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How (Not) to Run a Forecasting Competition: Incentives and Efficiency
Rafael Frongillo (University of Colorado Boulder)
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Coffee and Check-In
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The Invisible Hand of Prediction
Moritz Hardt (Max Planck Institute for Intelligent Systems)
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Break
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Communicating with Anecdotes
Nicole Immorlica (Microsoft Research)
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Lunch
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Reinforcement Learning for Complex Security Games and Beyond
Fei Fang (Carnegie Mellon Univeresity)
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Break
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Inference on Auctions with Weak Assumptions on Information
Vasilis Syrgkanis (Microsoft Research)