Publications

Authors with * contributed equally.

2024

• Can Neural Networks Achieve Optimal Computational-Statistical Tradeoff? An Analysis on Single-Index Model
  Siyu Chen*, Beining Wu*, Miao Lu, Zhuoran Yang, Tianhao Wang
  NeurIPS Workshop on Mathematics of Modern Machine Learning (M3L) 2024 Oral

• Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
  Zhihan Liu*, Miao Lu*, Shenao Zhang, Boyi Liu, Hongyi Guo, Yingxiang Yang, Jose Blanchet, Zhaoran Wang
  ICML Workshop on Aligning Reinforcement Learning Experimentalists and Theorists (ARLET) 2024
  Neural Information Processing Systems (NeurIPS) 2024 [PDF] [Code]
  

• Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm
  Miao Lu*, Han Zhong*, Tong Zhang, Jose Blanchet
  ICML Workshop on Aligning Reinforcement Learning Experimentalists and Theorists (ARLET) 2024
  Neural Information Processing Systems (NeurIPS) 2024 [PDF] [Slides]
  

• Benign Oscillation of Stochastic Gradient Descent with Large Learning Rates
  Miao Lu*, Beining Wu*, Xiaodong Yang, Difan Zou
  NeurIPS Workshop on Mathematics of Modern Machine Learning (M3L) 2023
  International Conference on Learning Representations (ICLR) 2024 [PDF] [Poster]

Before 2024

• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
  Zhihan Liu*, Miao Lu*, Wei Xiong*, Han Zhong, Hao Hu, Shenao Zhang, Sirui Zheng, Zhuoran Yang, Zhaoran Wang
  Neural Information Processing Systems (NeurIPS) 2023 Spotlight [PDF] [Code] [Slides]
  

• Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
  Jose Blanchet, Miao Lu, Tong Zhang, Han Zhong (alphabetical)
  Neural Information Processing Systems (NeurIPS) 2023 (short version)
  ArXiv preprint (long version, under review), Aug, 2023 [PDF] [Slides]
  

• Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes
  Miao Lu, Yifei Min, Zhaoran Wang, Zhuoran Yang
  International Conference on Learning Representations (ICLR) 2023 [PDF] [Slides]

• Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy
  Zhihan Liu*, Miao Lu*, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang
  International Conference on Machine Learning (ICML) 2022 [PDF] [Code] [Slides]
  

• Learning Pruning-Friendly Networks via Frank-Wolfe: One-Shot, Any-Sparsity, and No Retraining
  Miao Lu*, Xiaolong Luo*, Tianlong Chen, Wuyang Chen, Dong Liu, Zhangyang Wang
  International Conference on Learning Representations (ICLR) 2022 Spotlight [PDF] [Code] [Slides]
  

• Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization
  Yufei Kuang, Miao Lu, Jie Wang, Qi Zhou, Bin Li, Houqiang Li
  Association for the Advancement of Artificial Intelligence (AAAI) 2022 [PDF] [Code] [Slides]