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Eliciting Honest Information From Authors Using Sequential Review

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Speaker: Yichi Zhang (University of Michigan) Talk Title: Eliciting Honest Information From Authors Using Sequential Review Abstract: In the setting of conference peer review, the conference aims to accept high-quality papers and reject low-quality papers...

Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games

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Speaker: Brian Zhang (CMU) Title: Computing Optimal Equilibria and Mechanisms via Learning in Zero-Sum Extensive-Form Games Abstract: We introduce a unified framework for understanding optimal equilibria in general games by explicitly augmenting the game...

Learning Bayes-Nash Equilibria in Auctions and Contests

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Speaker: Martin Bichler Abstract: Equilibrium problems in Bayesian auction games can be described as systems of differential equations. Depending on the model assumptions, these equations might be such that we do not have an exact mathematical solution...

Contract Design in Combinatorial Settings

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Speaker: Tomer Ezra (Harvard) Title: Contract Design in Combinatorial Settings Abstract: We study two combinatorial settings of the contract design problem, in which a principal wants to delegate the execution of a costly task. In the first setting, the...

Reveal Planning Capability of Autoregressive Learning in Large Language Models

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Time & Location: March 29, Friday, 1:30pm - 2:30 pm, at SEC 1.413 1st Speaker: Shi Feng (Harvard) Title: Reveal Planning Capability of Autoregressive Learning in Large Language Models Abstract: Search and planning are fundamental constructs of human...

Reveal Planning Capability of Autoregressive Learning in Large Language Models and Computing Voting Rules with Elicited Incomplete Votes

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1st Speaker: Shi Feng (Harvard) Title: Reveal Planning Capability of Autoregressive Learning in Large Language Models Abstract: Search and planning are fundamental constructs of human intelligence, involved in almost every aspect of our daily lives, from...

Reinforcement Learning Meets Bilevel Optimization: Learning Leader-Follower Games with Sample Efficiency

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Speaker: Zhuoran Yang Title: Reinforcement Learning Meets Bilevel Optimization: Learning Leader-Follower Games with Sample Efficiency Abstract: In this talk, I will introduce methods that modify the optimism principle for reinforcement learning in leader...

Automated Social Science: A Structural Causal Model-Based Approach

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Speaker: John Horton (MIT) Title: Automated Social Science: A Structural Causal Model-Based Approach Abstract: We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by...

When Personalization Harms Performance

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Speaker: Berk Ustun (UCSD) Title: When Personalization Harms Performance Abstract: Clinical prediction models often encode group attributes like sex, age, and HIV status for personalization – i.e., to assign more accurate predictions to heterogeneous...

Competitive market design for transaction sequencing

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Speaker: Yonatan Sompolinsky (Harvard) Abstract: We tackle the problem of Miners Extracted Value (MEV), where a transaction sequencer extracts profit off users, by frontrunning pending transactions, sandwiching others, or carrying out any other form of...

Delegated Classification

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Speaker: Eden Saig Title: Delegated Classification Abstract: What happens when machine learning is outsourced to profit-maximizing agents? In this work, we propose a theoretical framework for incentive-aware delegation of machine learning tasks. We model...

Self-Resolving Prediction Markets for Unverifiable Outcomes

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Abstract: Prediction markets elicit and aggregate beliefs by paying agents based on how close their predictions are to a verifiable future outcome. However, outcomes of many important questions are difficult to verify or unverifiable, in that the ground...