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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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    • 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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    • WOMAC: A Mechanism For Prediction Competitions
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    • Learning a Game by Paying the Agents
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    • On Clearing Prices in Matching Markets: A Simple Characterization without Duality
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A Unified Approach to Submodular Maximization Under Noise

Dec 10, 2025·
(Alphabet) Kshipra Bhawalkar
,
Yang Cai
,
Zhe Feng
,
Christopher Liaw
,
Tao Lin
· 0 min read
PDF Cite
Last updated on Dec 10, 2025
Submodular Optimization
Generalized Principal-Agent Problem with a Learning Agent Apr 24, 2025 →

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

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