Publications
Check my Google Scholar profile for more information! (* stands for equal contribution.)
Economics and Computer Science
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Market Design for AI: Beyond the Copyright Binary
, Maryam Farboodi, Negin Golrezaei, and Sepehr Shahshahani.Early versions accepted to...
- Wharton Accountable AI Research Conference (Feb, 2026)
- Stanford Market Design in the Age of AI Conference (Feb, 2026)
- The 11th Marketplace Innovation Workshop (May, 2026)
- Informs M&SOM Conference Service Management SIG (Jul, 2026)
- Conference of Institutional & Organizational Economics (Jul, 2026)
- NBER Summer Institute Law and Economics Workshop (Jul, 2026)
- Informs Annual Meeting (Nov, 2026)
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Incentive-Aware Dynamic Resource Allocation under Long-Term Cost Constraints
, Negin Golrezaei, and Patrick Jaillet.
Early version accepted to NeurIPS 2025.
1st place in ACM Student Research Competition (SRC), SIGMETRICS 2025.Presentations at...
- UMass Amherst Theory Seminar (Oct, 2025)
- Informs Annual Meeting (Oct, 2025)
- International Seminar on Foundational AI (Nov, 2025)
- TwoSigma PhD Fellowship Reception (Feb, 2026)
- Citadel Securities PhD Summit (Apr, 2026)
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Non-Monetary Mechanism Design without Priors: Achieving Efficiency via Adaptive Costly Audits
, Moïse Blanchard, and Patrick Jaillet.
Under review at Operations Research.
Early version accepted to COLT 2025.
Bandits and Online Learning
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Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts
, Negin Golrezaei, and Patrick Jaillet. -
Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks
and Longbo Huang.
In Proceedings of the ACM on Measurement and Analysis of Computing Systems, 8(3):31, 2024.
Best Paper Award of ACM SIGMETRICS 2025. -
uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs
Yu Chen*, Jiatai Huang*, , and Longbo Huang. -
Variance-Aware Sparse Linear Bandits
, Ruosong Wang, and Simon S. Du.
Reinforcement Learning Theory
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Learning Adversarial Continuous MDPs with Bandit Feedback and Unknown Transitions
Aarush Kulkarni, Khang Nguyen, Ricardo Parada, Kenny Guo, William Chang, and . -
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
, Qiwen Cui, and Simon S. Du. -
Refined Regret for Adversarial MDPs with Linear Function Approximation
, Haipeng Luo, Chen-Yu Wei, and Julian Zimmert.
Deep Learning Theory
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Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
Kwangjun Ahn, Zhiyu Zhang, Yunbum Kook, and .