Selected Publications

For a complete list, please refer to my Google Scholar profile.
The publications are listed reverse chronologically.
* indicates equal contribution

Preprints

• Continuous K-Max Bandits
  Yu Chen*, Siwei Wang*, Longbo Huang, and Wei Chen.
  In submission.

Publications

• Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes
  Yu Chen, Yuhao Liu, Jiatai Huang, Yihan Du, and Longbo Huang.
  Forty-third International Conference on Machine Learning [ICML], 2026.

• PowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matching
  Ruishuo Chen, Yu Chen, Zhuoran Li, and Longbo Huang.
  Forty-third International Conference on Machine Learning [ICML], 2026.

• Finite-time Convergence Analysis of Actor-Critic with Evolving Reward
  Rui Hu, Yu Chen, and Longbo Huang.
  Forty-third International Conference on Machine Learning [ICML], 2026.

• Finite-Time Convergence Analysis of ODE-based Generative Models for Stochastic Interpolants
  Yuhao Liu, Yu Chen, Rui Hu, and Longbo Huang.
  The Fourteenth International Conference on Learning Representations [ICLR], 2026.

• Finite-Time Analysis of Discrete-Time Stochastic Interpolants
  Yuhao Liu, Yu Chen, Rui Hu, Longbo Huang.
  Forty-second International Conference on Machine Learning [ICML], 2025.

• uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs
  Yu Chen*, Jiatai Huang*, Yan Dai*, and Longbo Huang.
  The Thirteenth International Conference on Learning Representation [ICLR], 2025. (Spotlight, Top 5%)

• Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
  Yu Chen*, Xiangcheng Zhang*, Siwei Wang, and Longbo Huang.
  Forty-first International Conference on Machine Learning [ICML], 2024.

• Provably Efficient Partially Observable Risk-Sensitive Reinforcement Learning with Hindsight Observation
  Tonghe Zhang*, Yu Chen*, and Longbo Huang
  Forty-first International Conference on Machine Learning [ICML], 2024.

• Multi-User Delay-Constrained Scheduling With Deep Recurrent Reinforcement Learning
  Pihe Hu, Yu Chen, Ling Pan, Zhixuan Fang, Fu Xiao, and Longbo Huang
  IEEE/ACM Transactions on Networking [TON], 2024.

• Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
  Yu Chen, Yihan Du, Pihe Hu, Siwei Wang, Desheng Wu, and Longbo Huang
  The Twelfth International Conference on Learning Representations [ICLR], 2024.

• Towards Minimax Optimal Reward-free Reinforcement Learning in Linear MDPs
  Pihe Hu*, Yu Chen*, and Longbo Huang
  The Eleventh International Conference on Learning Representations [ICLR], 2023.

• Effective multi-user delay-constrained scheduling with deep recurrent reinforcement learning
  Pihe Hu, Ling Pan, Yu Chen, Zhixuan Fang, and Longbo Huang
  Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing [MobiHoc], 2022.

• Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
  Pihe Hu, Yu Chen, and Longbo Huang
  Proceedings of the 39th International Conference on Machine Learning [ICML], 2022.