Tao Lin
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    • Generalized Principal-Agent Problem with a Learning Agent
    • Information Design with Unknown Prior
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    • Bias Detection via Signaling
    • Multi-Sender Persuasion: A Computational Perspective
    • Learning Thresholds with Latent Values and Censored Feedback
    • Sample Complexity of Forecast Aggregation
    • From Monopoly to Competition: Optimal Contests Prevail
    • Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions
    • How Many Representatives Do We Need? The Optimal Size of a Congress Voting on Binary Issues
    • Learning Utilities and Equilibria in Non-Truthful Auctions
    • A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling
    • Private Data Manipulation in Optimal Sponsored Search Auction
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    • How to Avoid Polarization in Recommender Systems with Dual Influence?
    • Bayesian Persuasion with a Learning Agent
    • Generalized Principal-Agent Problem with a Learning Agent
    • Private Data Manipulation in Sponsored Search Auctions
    • Sample Complexity of Forecast Aggregation
    • Persuading a Behavioral Agent: Approximately Best Responding and Learning
    • Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions
    • Robustness of Empirical Revenue Maximization in Auction Learning
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    • On Clearing Prices in Matching Markets: A Simple Characterization without Duality
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Information Design with Unknown Prior

Jan 5, 2025·
Tao Lin
Tao Lin
,
Ce Li
· 0 min read
PDF Cite Video URL
Type
Conference paper
Publication
Proceedings of Innovations in Theoretical Computer Science (ITCS)
Last updated on Jan 5, 2025
Tao Lin
Authors
Tao Lin
PhD student in Computer Science
← Generalized Principal-Agent Problem with a Learning Agent Apr 24, 2025
User-Creator Feature Polarization in Recommender Systems with Dual Influence Dec 10, 2024 →

© 2025 Me. This work is licensed under CC BY NC ND 4.0

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