Wenhao Zhan

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I am a Ph.D. student at Princeton University advised by Professor Jason D. Lee and Yuxin Chen.
Before that, I received my Bachelor's Degree in Electronic Engineering from Tsinghua University.

Office: Friend Center 307, Princeton, NJ.
Email: wenhao.zhan@princeton.edu

Research

My research interests include

  • Reinforcement Learning

  • Statistics

Publications

(* = equal contribution, + = equal contribution and random order)

  1. Z. Zhang, W. Zhan, Y. Chen, S. S. Du, J. D. Lee, "Optimal Multi-Distribution Learning", Preprint.

  2. W. Zhan, M. Uehara, W. Sun, J. D. Lee, "Provable Reward-Agnostic Preference-Based Reinforcement Learning", accepted to ICLR 2024 Spotlight.

  3. W. Zhan*, M. Uehara*, N. Kallus, J. D. Lee, W. Sun, "Provable Offline Preference-Based Reinforcement Learning", accepted to ICLR 2024 Spotlight.

  4. Y. Zhao+, W. Zhan+, X. Hu+, H. Leung, F. Farnia, W. Sun, J. D. Lee, "Provably Efficient CVaR RL in Low-rank MDPs", accepted to ICLR 2024.

  5. G. Li*, W. Zhan*, J. D. Lee, Y. Chi, Y. Chen, "Reward-agnostic Fine-tuning: Provable Statistical Benefits of Hybrid Reinforcement Learning", Neurips 2023.

  6. W. Zhan*, S. Cen*, B. Huang, Y. Chen, J. D. Lee, Y. Chi, "Policy Mirror Descent for Regularized Reinforcement Learning: A Generalized Framework with Linear Convergence", SIAM Journal on Optimization, 2023.

  7. W. Zhan, M. Uehara, W. Sun, J. D. Lee, "PAC Reinforcement Learning for Predictive State Representations", ICLR 2023.

  8. W. Zhan, J. D. Lee, Z. Yang, "Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games", ICLR 2023.

  9. W. Zhan, B. Huang, A. Huang, N. Jiang, J. D. Lee, "Offline Reinforcement Learning with Realizability and Single-policy Concentrability", COLT 2022.

  10. C. Z. Lee, L. P. Barnes, W. Zhan, A. Özgür, "Over-the-Air Statistical Estimation of Sparse Models", GLOBECOM 2021.

  11. W. Zhan, H. Tang, J. Wang, "Delay Optimal Cross-Layer Scheduling Over Markov Channels with Power Constraint", BMSB 2020.

Teaching

  • Spring 2024: Foundations of Reinforcement Learning, as TA (Princeton, Instructor: Prof. Chi Jin).

  • Fall 2022: Theory of Weakly Supervised Learning, as TA (Princeton, Instructor: Prof. Jason D. Lee).