Pan Xu

Assistant Professor of Biostatistics & Bioinformatics

My research is centered around Machine Learning, with broad interests in the areas of Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, High Dimensional Statistics, and their applications to real-world problems including Bioinformatics and Healthcare. My research goal is to develop computationally- and data-efficient machine learning algorithms with both strong empirical performance and theoretical guarantees.

Appointments and Affiliations

  • Assistant Professor of Biostatistics & Bioinformatics
  • Assistant Professor of Computer Science
  • Assistant Professor in the Department of Electrical and Computer Engineering

Contact Information

Education

  • Ph.D. University of California, Los Angeles, 2021

Research Interests

Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics

Courses Taught

  • COMPSCI 391: Independent Study
  • BIOSTAT 825: Foundation of Reinforcement Learning

Representative Publications

  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Liu, Zhishuai, and Pan Xu. “Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation,” February 23, 2024.
  • Jin, Tianyuan, Hao-Lun Hsu, William Chang, and Pan Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs,” December 24, 2023.
  • Qin, Zhen, Zhishuai Liu, and Pan Xu. “Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization,” October 24, 2023.
  • Jin, Tianyuan, Yu Yang, Jing Tang, Xiaokui Xiao, and Pan Xu. “Optimal Batched Best Arm Identification,” October 21, 2023.