Miao Lu
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About Me Welcome to my homepage! I am a Ph.D. student of the Department of Management Science and Engineering (MS&E) at Stanford University, majoring in Operations Research. Prior to that, I obtained my Bachelor's degree in Probability and Statistics from the University of Science and Technology of China (USTC), where I won the Guo Moruo scholarship, the highest honor awarding undergraduates of USTC. Research Interests My ultimate goal of research is to (i) develop the theoretical foundations of the next generation artificial intelligence (AI) techniques and (ii) make the best use of AI to effectively address challenging managerial and societal problems. Contact Information E-mail addresses: miaolu@stanford.edu, lumiao@mail.ustc.edu.cn Here are my [Google Scholar] [GitHub] [LinkedIn] [CV] |
Recent News
(Dec. 2024) I am presenting our work ‘‘Can Neural Networks Achieve Optimal Computational-Statistical Tradeoff? An Analysis on Single-Index Model’’ at NeurIPS 2024 M3L Workshop. See you at Vancouver!
(Oct. 2024) I am presenting our work ‘‘Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm’’ at INFORMS 2024. See you at Seattle!
(Sep. 2024) Our two papers (distributionally robust RL with interactive data collection and provably mitigating overoptimization in RLHF) are accepted to Neural Information Processing Systems (NeurIPS) 2024.
Selected Publications
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
with Zhihan Liu, Shenao Zhang, Boyi Liu, Hongyi Guo, Yingxiang Yang, Jose Blanchet, Zhaoran Wang
Neural Information Processing Systems (NeurIPS) 2024
Benign Oscillation of Stochastic Gradient Descent with Large Learning Rates
with Beining Wu, Xiaodong Yang, Difan Zou
International Conference on Learning Representations (ICLR) 2024
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
with Zhihan Liu, Wei Xiong, Han Zhong, Hao Hu, Shenao Zhang, Sirui Zheng, Zhuoran Yang, Zhaoran Wang
Neural Information Processing Systems (NeurIPS) 2023
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
with Jose Blanchet, Tong Zhang, Han Zhong
Neural Information Processing Systems (NeurIPS) 2023