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Efficient Universal Estimators For Symmetric Property Estimation
Kirankumar Shiragur (Stanford University)
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Mechanism Design Via Machine Learning: Overfitting, Incentives, and Privacy
Ellen Vitercik (UC Berkeley)
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Fair And Reliable Machine Learning For High-Stakes Applications:approaches Using Information Theory
Sanghamitra Dutta (JP Morgan)
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Information, Learning, And Incentive Design For Societal Networks
Manxi Wu (UC Berkeley)
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Break
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Causal Inference And Data-Driven Decision-Making
Angela Zhou (UC Berkeley)
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Stabilizing Q-Learning With Linear Architectures For Provable Efficient Learning
Andrea Zanette (UC Berkeley)
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Causal Inference With Corrupted Data: Measurement Error, Missing Values, Discretization, And Differential Privacy
Rahul Singh (MIT)
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Lunch
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Choosing Between Dags Can Be Hard
Richard Guo (University of Cambridge)
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Fundamental Limits Of Learning In Data-Driven Problems
Yanjun Han (Simons Institute)
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Algorithmic Advances For The Design And Analysis Of Randomized Control Trials
Christopher Harshaw (Yale)
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Multi-Agent Reinforcement Learning In Stochastic Games: From Alphago To Robust Control
Kaiqing Zhang (MIT)
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Break
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Learning In The Presence Of Strategic Agents: Dynamics, Equilibria, And Convergence
Eric Mazumdar (Caltech)
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Causal Inference For Socio-Economic & Engineering Systems
Anish Agarwal (MIT EECS)
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Teamwork Makes The Von Neumann Work: Two Team Zero Sum Games
Emmanouil Vlatakis (Columbia University)
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Nonparametric Density Estimation and Convergence of GANs under Besov IPMs
Ananya Uppal (University of Texas at Austin)
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Reception