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

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

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

Representative Publications

  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.
  • 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.
  • Shen, Yi, Pan Xu, and Michael M. Zavlanos. “Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits,” September 15, 2023.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” In SIGMETRICS 2023 - Abstract Proceedings of the 2023 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 83–84, 2023. https://doi.org/10.1145/3578338.3593545.
  • Zhang, Y., G. Qu, P. Xu, Y. Lin, Z. Chen, and A. Wierman. “Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning.” Performance Evaluation Review 51, no. 1 (June 19, 2023): 83–84. https://doi.org/10.1145/3606376.3593545.