Semiparametric Differential Graph Models. - Pan Xu 002; Quanquan Gu (NIPS, 2016)

Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates. - Lu Tian; Pan Xu 002; Quanquan Gu (UAI, 2016)

Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent. - Pan Xu 002; Tingting Zhang; Quanquan Gu (AISTATS, 2017)

Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. - Aditya Chaudhry; Pan Xu 002; Quanquan Gu (ICML, 2017)

Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization. - Pan Xu 002; Jian Ma 004; Quanquan Gu (NIPS, 2017)

Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. - Pan Xu 002; Jinghui Chen; Quanquan Gu (CoRR, 2017)

Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations. - Pan Xu 002; Jian Ma 004; Quanquan Gu (CoRR, 2017)

Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima. - Yaodong Yu; Pan Xu 002; Quanquan Gu (CoRR, 2017)

Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. - Pan Xu 002; Tianhao Wang 002; Quanquan Gu (AISTATS, 2018)

Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. - Jinghui Chen; Pan Xu 002; Lingxiao Wang 001; Jian Ma 004; Quanquan Gu (ICML, 2018)

Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions. - Pan Xu 002; Tianhao Wang 002; Quanquan Gu (ICML, 2018)

Stochastic Variance-Reduced Cubic Regularized Newton Method. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (ICML, 2018)

Stochastic Variance-Reduced Hamilton Monte Carlo Methods. - Difan Zou; Pan Xu 002; Quanquan Gu (ICML, 2018)

Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. - Pan Xu 002; Jinghui Chen; Difan Zou; Quanquan Gu (NeurIPS, 2018)

Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. - Yaodong Yu; Pan Xu 002; Quanquan Gu (NeurIPS, 2018)

Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (NeurIPS, 2018)

Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. - Difan Zou; Pan Xu 002; Quanquan Gu (UAI, 2018)

Finding Local Minima via Stochastic Nested Variance Reduction. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (CoRR, 2018)

Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (CoRR, 2018)

Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. - Difan Zou; Pan Xu 002; Quanquan Gu (AISTATS, 2019)

Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. - Difan Zou; Pan Xu 002; Quanquan Gu (NeurIPS, 2019)

An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. - Pan Xu 002; Felicia Gao; Quanquan Gu (UAI, 2019)

Stochastic Variance-Reduced Cubic Regularization Methods. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (J. Mach. Learn. Res., 2019)

Rank Aggregation via Heterogeneous Thurstone Preference Models. - Tao Jin 002; Pan Xu 002; Quanquan Gu; Farzad Farnoud (AAAI, 2020)

Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. - Pan Xu 002; Felicia Gao; Quanquan Gu (ICLR, 2020)

A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. - Pan Xu 002; Quanquan Gu (ICML, 2020)

A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods. - Yue Wu; Weitong Zhang; Pan Xu 002; Quanquan Gu (NeurIPS, 2020)

Stochastic Nested Variance Reduction for Nonconvex Optimization. - Dongruo Zhou; Pan Xu 002; Quanquan Gu (J. Mach. Learn. Res., 2020)

Double Explore-then-Commit: Asymptotic Optimality and Beyond. - Tianyuan Jin; Pan Xu 002; Xiaokui Xiao; Quanquan Gu (COLT, 2021)

MOTS: Minimax Optimal Thompson Sampling. - Tianyuan Jin; Pan Xu 002; Jieming Shi 001; Xiaokui Xiao; Quanquan Gu (ICML, 2021)

Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. - Tianyuan Jin; Jing Tang 004; Pan Xu 002; Keke Huang; Xiaokui Xiao; Quanquan Gu (ICML, 2021)

Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. - Difan Zou; Pan Xu 002; Quanquan Gu (UAI, 2021)

Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. - Yue Wu; Tao Jin 002; Hao Lou; Pan Xu 002; Farzad Farnoud; Quanquan Gu (AISTATS, 2022)

Neural Contextual Bandits with Deep Representation and Shallow Exploration. - Pan Xu 002; Zheng Wen; Handong Zhao; Quanquan Gu (ICLR, 2022)

Langevin Monte Carlo for Contextual Bandits. - Pan Xu 002; Hongkai Zheng; Eric V. Mazumdar; Kamyar Azizzadenesheli; Animashree Anandkumar (ICML, 2022)

Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits. - Tianyuan Jin; Pan Xu 002; Xiaokui Xiao; Anima Anandkumar (NeurIPS, 2022)

Active Ranking without Strong Stochastic Transitivity. - Hao Lou; Tao Jin 002; Yue Wu; Pan Xu 002; Quanquan Gu; Farzad Farnoud (NeurIPS, 2022)

Distributionally Robust Policy Gradient for Offline Contextual Bandits. - Zhouhao Yang; Yihong Guo; Pan Xu 002; Anqi Liu; Animashree Anandkumar (AISTATS, 2023)

Queer In AI: A Case Study in Community-Led Participatory AI. - 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 009; et al. (FAccT, 2023)

Thompson Sampling with Less Exploration is Fast and Optimal. - Tianyuan Jin; Xianglin Yang; Xiaokui Xiao; Pan Xu 002 (ICML, 2023)

Queer In AI: A Case Study in Community-Led Participatory AI. - Anaelia Ovalle; Arjun Subramonian; Ashwin Singh; Claas Voelcker; Danica J. Sutherland; Davide Locatelli; Eva Breznik; Filip Klubicka; Hang Yuan; Hetvi Jethwani; Huan Zhang 009; Jaidev Shriram; et al. (CoRR, 2023)

Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization. - Zhen Qin; Zhishuai Liu; Pan Xu 002 (CoRR, 2023)

Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning. - Yizhou Zhang; Guannan Qu; Pan Xu 002; Yiheng Lin 001; Zaiwei Chen; Adam Wierman (Proc. ACM Meas. Anal. Comput. Syst., 2023)

Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphs. - Tianyuan Jin; Hao-Lun Hsu; William Chang; Pan Xu 002 (AAAI, 2024)

Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation. - Zhishuai Liu; Pan Xu 002 (AISTATS, 2024)

Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo. - Haque Ishfaq; Qingfeng Lan; Pan Xu 002; A. Rupam Mahmood; Doina Precup; Anima Anandkumar; Kamyar Azizzadenesheli (ICLR, 2024)

Optimal Batched Linear Bandits. - Xuanfei Ren; Tianyuan Jin; Pan Xu 002 (ICML, 2024)

Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning. - Hao-Lun Hsu; Weixin Wang; Miroslav Pajic; Pan Xu 002 (NeurIPS, 2024)

Optimal Batched Best Arm Identification. - Tianyuan Jin; Yu Yang 001; Jing Tang 004; Xiaokui Xiao; Pan Xu 002 (NeurIPS, 2024)

Minimax Optimal and Computationally Efficient Algorithms for Distributionally Robust Offline Reinforcement Learning. - Zhishuai Liu; Pan Xu 002 (NeurIPS, 2024)

More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling. - Haque Ishfaq; Yixin Tan; Yu Yang 001; Qingfeng Lan; Jianfeng Lu; A. Rupam Mahmood; Doina Precup; Pan Xu 002 (RLJ, 2024)

Efficient and robust sequential decision making algorithms. - Pan Xu 002 (AI Mag., 2024)

Pre-trained Language Models Improve the Few-shot Prompt Ability of Decision Transformer. - Yu Yang 001; Pan Xu 002 (CoRR, 2024)

Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning. - Zhishuai Liu; Weixin Wang; Pan Xu 002 (CoRR, 2024)

Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning. - Ruhan Wang; Yu Yang 001; Zhishuai Liu; Dongruo Zhou; Pan Xu 002 (CoRR, 2024)

Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization. - Cheng Tang; Zhishuai Liu; Pan Xu 002 (CoRR, 2024)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021. - 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; et al. (PLoS Comput. Biol., 2024)

Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning. - Yihe Deng; Ruochi Zhang; Pan Xu 002; Jian Ma 004; Quanquan Gu (Trans. Mach. Learn. Res., 2024)

How to Provably Improve Return Conditioned Supervised Learning? - Zhishuai Liu; Yu Yang 001; Ruhan Wang; Pan Xu 002; Dongruo Zhou (CoRR, 2025)