Xu, Pan

65 publications

JAIR 2025 A New Regret-Analysis Framework for Budgeted Multi-Armed Bandits Evan Yifan Xu, Pan Xu

NeurIPS 2025 Linear Mixture Distributionally Robust Markov Decision Processes Zhishuai Liu, Pan Xu

JAIR 2025 Optimizing Relevance and Diversity in Online Matching Markets: A Time-Adaptive Attenuation Approach Evan Yifan Xu, Pan Xu

TMLR 2025 Pre-Trained Language Models Improve the Few-Shot Prompt Ability of Decision Transformer Yu Yang, Pan Xu

ICML 2025 Robust Offline Reinforcement Learning with Linearly Structured $f$-Divergence Regularization Cheng Tang, Zhishuai Liu, Pan Xu

ICML 2025 Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction Yiting He, Zhishuai Liu, Weixin Wang, Pan Xu

IJCAI 2024 Design a Win-Win Strategy That Is Fair to Both Service Providers and Tasks When Rejection Is Not an Option Yohai Trabelsi, Pan Xu, Sarit Kraus

AISTATS 2024 Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation Zhishuai Liu, Pan Xu

JAIR 2024 Exploring the Tradeoff Between System Profit and Income Equality Among Ride-Hailing Drivers Evan Yifan Xu, Pan Xu

AAAI 2024 Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs Tianyuan Jin, Hao-Lun Hsu, William Chang, Pan Xu

NeurIPS 2024 Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning Zhishuai Liu, Pan Xu

NeurIPS 2024 Off-Dynamics Reinforcement Learning via Domain Adaptation and Reward Augmented Imitation Yihong Guo, Yixuan Wang, Yuanyuan Shi, Pan Xu, Anqi Liu

NeurIPS 2024 Optimal Batched Best Arm Identification Tianyuan Jin, Yu Yang, Jing Tang, Xiaokui Xiao, Pan Xu

ICML 2024 Optimal Batched Linear Bandits Xuanfei Ren, Tianyuan Jin, Pan Xu

ICML 2024 Parameter-Dependent Competitive Analysis for Online Capacitated Coverage Maximization Through Boostings and Attenuations Pan Xu

TMLR 2024 Pre-Trained Hypergraph Convolutional Neural Networks with Self-Supervised Learning Yihe Deng, Ruochi Zhang, Pan Xu, Jian Ma, Quanquan Gu

NeurIPSW 2024 Pre-Trained Language Models Improve the Few-Shot Prompt Ability of Decision Transformer Yu Yang, Pan Xu

ICML 2024 Promoting External and Internal Equities Under Ex-Ante/Ex-Post Metrics in Online Resource Allocation Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu

NeurIPS 2024 Promoting Fairness Among Dynamic Agents in Online-Matching Markets Under Known Stationary Arrival Distributions Will Ma, Pan Xu

ICLR 2024 Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo Haque Ishfaq, Qingfeng Lan, Pan Xu, A. Rupam Mahmood, Doina Precup, Anima Anandkumar, Kamyar Azizzadenesheli

NeurIPS 2024 Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu

TMLR 2024 Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits Yi Shen, Pan Xu, Michael Zavlanos

AISTATS 2023 Distributionally Robust Policy Gradient for Offline Contextual Bandits Zhouhao Yang, Yihong Guo, Pan Xu, Anqi Liu, Animashree Anandkumar

AAAI 2023 Equity Promotion in Public Transportation Anik Pramanik, Pan Xu, Yifan Xu

ICML 2023 Thompson Sampling with Less Exploration Is Fast and Optimal Tianyuan Jin, Xianglin Yang, Xiaokui Xiao, Pan Xu

AISTATS 2022 Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu

NeurIPS 2022 Active Ranking Without Strong Stochastic Transitivity Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud

AAAI 2022 Equity Promotion in Online Resource Allocation Pan Xu, Yifan Xu

NeurIPS 2022 Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits Tianyuan Jin, Pan Xu, Xiaokui Xiao, Anima Anandkumar

ICML 2022 Langevin Monte Carlo for Contextual Bandits Pan Xu, Hongkai Zheng, Eric V Mazumdar, Kamyar Azizzadenesheli, Animashree Anandkumar

ICLR 2022 Neural Contextual Bandits with Deep Representation and Shallow Exploration Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu

ICML 2021 Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu

COLT 2021 Double Explore-Then-Commit: Asymptotic Optimality and Beyond Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu

UAI 2021 Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling Difan Zou, Pan Xu, Quanquan Gu

ICML 2021 MOTS: Minimax Optimal Thompson Sampling Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu

ICML 2020 A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation Pan Xu, Quanquan Gu

NeurIPS 2020 A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods Yue Frank Wu, Weitong Zhang, Pan Xu, Quanquan Gu

IJCAI 2020 A Unified Model for the Two-Stage Offline-Then-Online Resource Allocation Yifan Xu, Pan Xu, Jianping Pan, Jun Tao

AAAI 2020 Balancing the Tradeoff Between Profit and Fairness in Rideshare Platforms During High-Demand Hours Vedant Nanda, Pan Xu, Karthik Abinav Sankararaman, John P. Dickerson, Aravind Srinivasan

AAAI 2020 Rank Aggregation via Heterogeneous Thurstone Preference Models Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud

ICLR 2020 Sample Efficient Policy Gradient Methods with Recursive Variance Reduction Pan Xu, Felicia Gao, Quanquan Gu

JMLR 2020 Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou, Pan Xu, Quanquan Gu

IJCAI 2020 Trade the System Efficiency for the Income Equality of Drivers in Rideshare Yifan Xu, Pan Xu

AAAI 2019 A Unified Approach to Online Matching with Conflict-Aware Constraints Pan Xu, Yexuan Shi, Hao Cheng, John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Yongxin Tong, Leonidas Tsepenekas

UAI 2019 An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient Pan Xu, Felicia Gao, Quanquan Gu

AAAI 2019 Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu

AAAI 2019 Preference-Aware Task Assignment in On-Demand Taxi Dispatching: An Online Stable Matching Approach Boming Zhao, Pan Xu, Yexuan Shi, Yongxin Tong, Zimu Zhou, Yuxiang Zeng

AISTATS 2019 Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics Difan Zou, Pan Xu, Quanquan Gu

NeurIPS 2019 Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction Difan Zou, Pan Xu, Quanquan Gu

JMLR 2019 Stochastic Variance-Reduced Cubic Regularization Methods Dongruo Zhou, Pan Xu, Quanquan Gu

AISTATS 2018 Accelerated Stochastic Mirror Descent: From Continuous-Time Dynamics to Discrete-Time Algorithms Pan Xu, Tianhao Wang, Quanquan Gu

AAAI 2018 Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources John P. Dickerson, Karthik Abinav Sankararaman, Aravind Srinivasan, Pan Xu

ICML 2018 Continuous and Discrete-Time Accelerated Stochastic Mirror Descent for Strongly Convex Functions Pan Xu, Tianhao Wang, Quanquan Gu

ICML 2018 Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu

NeurIPS 2018 Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu

NeurIPS 2018 Stochastic Nested Variance Reduction for Nonconvex Optimization Dongruo Zhou, Pan Xu, Quanquan Gu

ICML 2018 Stochastic Variance-Reduced Cubic Regularized Newton Methods Dongruo Zhou, Pan Xu, Quanquan Gu

ICML 2018 Stochastic Variance-Reduced Hamilton Monte Carlo Methods Difan Zou, Pan Xu, Quanquan Gu

UAI 2018 Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics Difan Zou, Pan Xu, Quanquan Gu

NeurIPS 2018 Third-Order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima Yaodong Yu, Pan Xu, Quanquan Gu

AISTATS 2017 Efficient Algorithm for Sparse Tensor-Variate Gaussian Graphical Models via Gradient Descent Pan Xu, Tingting Zhang, Quanquan Gu

NeurIPS 2017 Speeding up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization Pan Xu, Jian Ma, Quanquan Gu

ICML 2017 Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference Aditya Chaudhry, Pan Xu, Quanquan Gu

UAI 2016 Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates Lu Tian, Pan Xu, Quanquan Gu

NeurIPS 2016 Semiparametric Differential Graph Models Pan Xu, Quanquan Gu