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

  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.
  • Ren, Xuanfei, Tianyuan Jin, and Pan Xu. “Optimal Batched Linear Bandits,” June 6, 2024.
  • Lopez, Velma K., Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O’Dea, Madeline Adee, Turgay Ayer, et al. “Challenges of COVID-19 Case Forecasting in the US, 2020-2021.” PLoS Comput Biol 20, no. 5 (May 2024): e1011200. https://doi.org/10.1371/journal.pcbi.1011200.
  • Hsu, Hao-Lun, Weixin Wang, Miroslav Pajic, and Pan Xu. “Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning,” April 16, 2024.
  • Jin, T., H. L. Hsu, W. Chang, and P. Xu. “Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs.” In Proceedings of the AAAI Conference on Artificial Intelligence, 38:12956–64, 2024. https://doi.org/10.1609/aaai.v38i11.29193.
  • Liu, Zhishuai, and Pan Xu. “Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning,” March 14, 2024.