Pan Xu - RankMe

98 papers A* 29A 10Journal 55Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning. CoRR Zhangjie Xia, Yu Yang, Pan Xu
2026	J	`jnl`	Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning. Trans. Mach. Learn. Res. Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou, Pan Xu
2025	J	`jnl`	How to Provably Improve Return Conditioned Supervised Learning? CoRR Zhishuai Liu, Yu Yang, Ruhan Wang, Pan Xu, Dongruo Zhou
2025	J	`jnl`	Linear Mixture Distributionally Robust Markov Decision Processes. CoRR Zhishuai Liu, Pan Xu
2025	J	`jnl`	MOBODY: Model Based Off-Dynamics Offline Reinforcement Learning. CoRR Yihong Guo, Yu Yang, Pan Xu, Anqi Liu
2025	J	`jnl`	Policy Regularized Distributionally Robust Markov Decision Processes with Linear Function Approximation. CoRR Jingwen Gu, Yiting He, Zhishuai Liu, Pan Xu
2025	J	`jnl`	Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer. Trans. Mach. Learn. Res. Yu Yang, Pan Xu
2025	J	`jnl`	Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Bandits. CoRR Jiazheng Sun, Weixin Wang, Pan Xu
2025	J	`jnl`	Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective. CoRR Weixin Wang, Haoyang Zheng, Guang Lin, Wei Deng, Pan Xu
2025	A*	`conf`	Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization. ICML Cheng Tang, Zhishuai Liu, Pan Xu
2025	A*	`conf`	Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction. ICML Yiting He, Zhishuai Liu, Weixin Wang, Pan Xu
2025	J	`jnl`	Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction. CoRR Yiting He, Zhishuai Liu, Weixin Wang, Pan Xu
2024	J	`jnl`	Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput. Biol. Velma K. Lopez, Estee Y. Cramer, Robert Pagano, John M. Drake, Eamon B. O'Dea, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Ozden O. Dalgic, Mary A. Ladd, Benjamin P. Linas, Peter P. Mueller, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph Chadi Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren A. Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan M. Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson
2024	A	`conf`	Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation. AISTATS Zhishuai Liu, Pan Xu
2024	J	`jnl`	Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation. CoRR Zhishuai Liu, Pan Xu
2024	J	`jnl`	Efficient and robust sequential decision making algorithms. AI Mag. Pan Xu
2024	A*	`conf`	Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. AAAI Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu
2024	A*	`conf`	Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning. NeurIPS Zhishuai Liu, Pan Xu
2024	J	`jnl`	Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning. CoRR Zhishuai Liu, Pan Xu
2024	J	`jnl`	More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. RLJ Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu
2024	J	`jnl`	More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. CoRR Haque Ishfaq, Yixin Tan, Yu Yang, Qingfeng Lan, Jianfeng Lu, A. Rupam Mahmood, Doina Precup, Pan Xu
2024	J	`jnl`	Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation. CoRR Yihong Guo, Yixuan Wang, Yuanyuan Shi, Pan Xu, Anqi Liu
2024	A*	`conf`	Optimal Batched Best Arm Identification. NeurIPS Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, Pan Xu
2024	A*	`conf`	Optimal Batched Linear Bandits. ICML Xuanfei Ren, Tianyuan Jin, Pan Xu
2024	J	`jnl`	Optimal Batched Linear Bandits. CoRR Xuanfei Ren, Tianyuan Jin, Pan Xu
2024	J	`jnl`	Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning. Trans. Mach. Learn. Res. Yihe Deng, Ruochi Zhang, Pan Xu, Jian Ma, Quanquan Gu
2024	J	`jnl`	Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer. CoRR Yu Yang, Pan Xu
2024	A*	`conf`	Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. ICLR Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
2024	A*	`conf`	Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning. NeurIPS Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu
2024	J	`jnl`	Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning. CoRR Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu
2024	J	`jnl`	Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning. CoRR Ruhan Wang, Yu Yang, Zhishuai Liu, Dongruo Zhou, Pan Xu
2024	J	`jnl`	Robust Offline Reinforcement Learning with Linearly Structured CoRR Cheng Tang, Zhishuai Liu, Pan Xu
2024	J	`jnl`	Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning. CoRR Zhishuai Liu, Weixin Wang, Pan Xu
2024	J	`jnl`	Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits. Trans. Mach. Learn. Res. Yi Shen, Pan Xu, Michael M. Zavlanos
2023	J	`jnl`	Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization. CoRR Zhen Qin, Zhishuai Liu, Pan Xu
2023	A	`conf`	Distributionally Robust Policy Gradient for Offline Contextual Bandits. AISTATS Zhouhao Yang, Yihong Guo, Pan Xu, Anqi Liu, Animashree Anandkumar
2023	J	`jnl`	Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. CoRR Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu
2023	—	`conf`	Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. SIGMETRICS (Abstracts) Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, Adam Wierman
2023	J	`jnl`	Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. Proc. ACM Meas. Anal. Comput. Syst. Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, Adam Wierman
2023	J	`jnl`	Optimal Batched Best Arm Identification. CoRR Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, Pan Xu
2023	J	`jnl`	Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. CoRR Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli
2023	—	`conf`	Queer In AI: A Case Study in Community-Led Participatory AI. FAccT Organizers Of QueerInAI, Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, Hetvi Jethwani, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, A Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, St John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark
2023	J	`jnl`	Queer In AI: A Case Study in Community-Led Participatory AI. CoRR Anaelia Ovalle, Arjun Subramonian, Ashwin Singh, Claas Voelcker, Danica J. Sutherland, Davide Locatelli, Eva Breznik, Filip Klubicka, Hang Yuan, Hetvi Jethwani, Huan Zhang, Jaidev Shriram, Kruno Lehman, Luca Soldaini, Maarten Sap, Marc Peter Deisenroth, Maria Leonor Pacheco, Maria Ryskina, Martin Mundt, Milind Agarwal, Nyx McLean, Pan Xu, A Pranav, Raj Korpan, Ruchira Ray, Sarah Mathew, Sarthak Arora, St John, Tanvi Anand, Vishakha Agrawal, William Agnew, Yanan Long, Zijie J. Wang, Zeerak Talat, Avijit Ghosh, Nathaniel Dennler, Michael Noseworthy, Sharvani Jha, Emi Baylor, Aditya Joshi, Natalia Y. Bilenko, Andrew McNamara, Raphael Gontijo Lopes, Alex Markham, Evyn Dong, Jackie Kay, Manu Saraswat, Nikhil Vytla, Luke Stark
2023	A*	`conf`	Thompson Sampling with Less Exploration is Fast and Optimal. ICML Tianyuan Jin, Xianglin Yang, Xiaokui Xiao, Pan Xu
2023	J	`jnl`	Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits. CoRR Yi Shen, Pan Xu, Michael M. Zavlanos
2022	A*	`conf`	Active Ranking without Strong Stochastic Transitivity. NeurIPS Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud
2022	A	`conf`	Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. AISTATS Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu
2022	A*	`conf`	Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. NeurIPS Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar
2022	J	`jnl`	Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. CoRR Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar
2022	J	`jnl`	Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. CoRR Yizhou Zhang, Guannan Qu, Pan Xu, Yiheng Lin, Zaiwei Chen, Adam Wierman
2022	A*	`conf`	Langevin Monte Carlo for Contextual Bandits. ICML Pan Xu, Hongkai Zheng, Eric V. Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar
2022	J	`jnl`	Langevin Monte Carlo for Contextual Bandits. CoRR Pan Xu, Hongkai Zheng, Eric Mazumdar, Kamyar Azizzadenesheli, Anima Anandkumar
2022	A*	`conf`	Neural Contextual Bandits with Deep Representation and Shallow Exploration. ICLR Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu
2021	J	`jnl`	Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. CoRR Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu
2021	A*	`conf`	Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. ICML Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu
2021	A*	`conf`	Double Explore-then-Commit: Asymptotic Optimality and Beyond. COLT Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu
2021	A	`conf`	Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. UAI Difan Zou, Pan Xu, Quanquan Gu
2021	A*	`conf`	MOTS: Minimax Optimal Thompson Sampling. ICML Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu
2020	J	`jnl`	A Finite Time Analysis of Two Time-Scale Actor Critic Methods. CoRR Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu
2020	A*	`conf`	A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. ICML Pan Xu, Quanquan Gu
2020	A*	`conf`	A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods. NeurIPS Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu
2020	J	`jnl`	Double Explore-then-Commit: Asymptotic Optimality and Beyond. CoRR Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu
2020	J	`jnl`	Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. CoRR Difan Zou, Pan Xu, Quanquan Gu
2020	J	`jnl`	MOTS: Minimax Optimal Thompson Sampling. CoRR Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu
2020	J	`jnl`	Neural Contextual Bandits with Deep Representation and Shallow Exploration. CoRR Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu
2020	A*	`conf`	Rank Aggregation via Heterogeneous Thurstone Preference Models. AAAI Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud
2020	A*	`conf`	Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. ICLR Pan Xu, Felicia Gao, Quanquan Gu
2020	J	`jnl`	Stochastic Nested Variance Reduction for Nonconvex Optimization. J. Mach. Learn. Res. Dongruo Zhou, Pan Xu, Quanquan Gu
2019	J	`jnl`	A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. CoRR Pan Xu, Quanquan Gu
2019	A	`conf`	An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. UAI Pan Xu, Felicia Gao, Quanquan Gu
2019	J	`jnl`	An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. CoRR Pan Xu, Felicia Gao, Quanquan Gu
2019	J	`jnl`	Rank Aggregation via Heterogeneous Thurstone Preference Models. CoRR Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud
2019	J	`jnl`	Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. CoRR Pan Xu, Felicia Gao, Quanquan Gu
2019	A	`conf`	Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. AISTATS Difan Zou, Pan Xu, Quanquan Gu
2019	A*	`conf`	Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. NeurIPS Difan Zou, Pan Xu, Quanquan Gu
2019	J	`jnl`	Stochastic Variance-Reduced Cubic Regularization Methods. J. Mach. Learn. Res. Dongruo Zhou, Pan Xu, Quanquan Gu
2018	A	`conf`	Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. AISTATS Pan Xu, Tianhao Wang, Quanquan Gu
2018	A*	`conf`	Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions. ICML Pan Xu, Tianhao Wang, Quanquan Gu
2018	A*	`conf`	Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. ICML Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu
2018	J	`jnl`	Finding Local Minima via Stochastic Nested Variance Reduction. CoRR Dongruo Zhou, Pan Xu, Quanquan Gu
2018	A*	`conf`	Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. NeurIPS Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu
2018	J	`jnl`	Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. CoRR Dongruo Zhou, Pan Xu, Quanquan Gu
2018	A*	`conf`	Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. NeurIPS Dongruo Zhou, Pan Xu, Quanquan Gu
2018	J	`jnl`	Stochastic Nested Variance Reduction for Nonconvex Optimization. CoRR Dongruo Zhou, Pan Xu, Quanquan Gu
2018	A*	`conf`	Stochastic Variance-Reduced Cubic Regularized Newton Method. ICML Dongruo Zhou, Pan Xu, Quanquan Gu
2018	J	`jnl`	Stochastic Variance-Reduced Cubic Regularized Newton Method. CoRR Dongruo Zhou, Pan Xu, Quanquan Gu
2018	A*	`conf`	Stochastic Variance-Reduced Hamilton Monte Carlo Methods. ICML Difan Zou, Pan Xu, Quanquan Gu
2018	J	`jnl`	Stochastic Variance-Reduced Hamilton Monte Carlo Methods. CoRR Difan Zou, Pan Xu, Quanquan Gu
2018	A	`conf`	Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. UAI Difan Zou, Pan Xu, Quanquan Gu
2018	A*	`conf`	Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. NeurIPS Yaodong Yu, Pan Xu, Quanquan Gu
2017	A	`conf`	Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent. AISTATS Pan Xu, Tingting Zhang, Quanquan Gu
2017	J	`jnl`	Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. CoRR Pan Xu, Jinghui Chen, Quanquan Gu
2017	—	`conf`	Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization. NIPS Pan Xu, Jian Ma, Quanquan Gu
2017	J	`jnl`	Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations. CoRR Pan Xu, Jian Ma, Quanquan Gu
2017	J	`jnl`	Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima. CoRR Yaodong Yu, Pan Xu, Quanquan Gu
2017	A*	`conf`	Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. ICML Aditya Chaudhry, Pan Xu, Quanquan Gu
2016	A	`conf`	Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates. UAI Lu Tian, Pan Xu, Quanquan Gu
2016	—	`conf`	Semiparametric Differential Graph Models. NIPS Pan Xu, Quanquan Gu