Spectral Gap Error Bounds for Improving CUR Matrix Decomposition and the Nyström Method. - David G. Anderson; Simon S. Du; Michael W. Mahoney; Christopher Melgaard; Kunming Wu; Ming Gu 002 (AISTATS, 2015)

An Improved Gap-Dependency Analysis of the Noisy Power Method. - Maria-Florina Balcan; Simon Shaolei Du; Yining Wang 001; Adams Wei Yu (COLT, 2016)

Efficient Nonparametric Smoothness Estimation. - Shashank Singh 005; Simon S. Du; Barnabás Póczos (NIPS, 2016)

Transformation Function Based Methods for Model Shift. - Simon Shaolei Du; Jayanth Koushik; Aarti Singh; Barnabás Póczos (CoRR, 2016)

Computationally Efficient Robust Sparse Estimation in High Dimensions. - Sivaraman Balakrishnan; Simon S. Du; Jerry Li 001; Aarti Singh (COLT, 2017)

High-Throughput Robotic Phenotyping of Energy Sorghum Crops. - Srinivasan Vijayarangan; Paloma Sodhi; Prathamesh Kini; James Bourne; Simon S. Du; Hanqi Sun; Barnabás Póczos; Dimitrios Apostolopoulos; David Wettergreen (FSR, 2017)

Stochastic Variance Reduction Methods for Policy Evaluation. - Simon S. Du; Jianshu Chen; Lihong Li 001; Lin Xiao; Dengyong Zhou (ICML, 2017)

Gradient Descent Can Take Exponential Time to Escape Saddle Points. - Simon S. Du; Chi Jin 001; Jason D. Lee; Michael I. Jordan; Aarti Singh; Barnabás Póczos (NIPS, 2017)

Hypothesis Transfer Learning via Transformation Functions. - Simon S. Du; Jayanth Koushik; Aarti Singh; Barnabás Póczos (NIPS, 2017)

On the Power of Truncated SVD for General High-rank Matrix Estimation Problems. - Simon S. Du; Yining Wang 001; Aarti Singh (NIPS, 2017)

Computationally Efficient Robust Estimation of Sparse Functionals. - Simon S. Du; Sivaraman Balakrishnan; Aarti Singh (CoRR, 2017)

Stochastic Zeroth-order Optimization in High Dimensions. - Yining Wang 001; Simon S. Du; Sivaraman Balakrishnan; Aarti Singh (AISTATS, 2018)

When is a Convolutional Filter Easy to Learn? - Simon S. Du; Jason D. Lee; Yuandong Tian (ICLR (Poster), 2018)

On the Power of Over-parametrization in Neural Networks with Quadratic Activation. - Simon S. Du; Jason D. Lee (ICML, 2018)

Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. - Simon S. Du; Jason D. Lee; Yuandong Tian; Aarti Singh; Barnabás Póczos (ICML, 2018)

Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms. - Yi Wu 013; Siddharth Srivastava 001; Nicholas Hay; Simon S. Du; Stuart Russell 001 (ICML, 2018)

Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow. - Xiao Zhang 016; Simon S. Du; Quanquan Gu (ICML, 2018)

Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced. - Simon S. Du; Wei Hu 014; Jason D. Lee (NeurIPS, 2018)

How Many Samples are Needed to Estimate a Convolutional Neural Network? - Simon S. Du; Yining Wang 001; Xiyu Zhai; Sivaraman Balakrishnan; Ruslan Salakhutdinov; Aarti Singh (NeurIPS, 2018)

Near-Linear Time Local Polynomial Nonparametric Estimation. - Yining Wang 001; Yi Wu 013; Simon S. Du (CoRR, 2018)

Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps. - Simon S. Du; Surbhi Goel (CoRR, 2018)

How Many Samples are Needed to Learn a Convolutional Neural Network? - Simon S. Du; Yining Wang 001; Xiyu Zhai; Sivaraman Balakrishnan; Ruslan Salakhutdinov; Aarti Singh (CoRR, 2018)

Robust Nonparametric Regression under Huber's ε-contamination Model. - Simon S. Du; Yining Wang 001; Sivaraman Balakrishnan; Pradeep Ravikumar; Aarti Singh (CoRR, 2018)

Linear Convergence of the Primal-Dual Gradient Method for Convex-Concave Saddle Point Problems without Strong Convexity. - Simon S. Du; Wei Hu 014 (AISTATS, 2019)

Gradient Descent Provably Optimizes Over-parameterized Neural Networks. - Simon S. Du; Xiyu Zhai; Barnabás Póczos; Aarti Singh (ICLR (Poster), 2019)

Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks. - Sanjeev Arora; Simon S. Du; Wei Hu 014; Zhiyuan Li 005; Ruosong Wang (ICML, 2019)

Width Provably Matters in Optimization for Deep Linear Neural Networks. - Simon S. Du; Wei Hu 014 (ICML, 2019)

Provably efficient RL with Rich Observations via Latent State Decoding. - Simon S. Du; Akshay Krishnamurthy; Nan Jiang 008; Alekh Agarwal; Miroslav Dudík; John Langford 001 (ICML, 2019)

Gradient Descent Finds Global Minima of Deep Neural Networks. - Simon S. Du; Jason D. Lee; Haochuan Li; Liwei Wang 001; Xiyu Zhai (ICML, 2019)

On Exact Computation with an Infinitely Wide Neural Net. - Sanjeev Arora; Simon S. Du; Wei Hu 014; Zhiyuan Li 005; Ruslan Salakhutdinov; Ruosong Wang (NeurIPS, 2019)

Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels. - Simon S. Du; Kangcheng Hou; Ruslan Salakhutdinov; Barnabás Póczos; Ruosong Wang; Keyulu Xu (NeurIPS, 2019)

Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle. - Simon S. Du; Yuping Luo; Ruosong Wang; Hanrui Zhang 001 (NeurIPS, 2019)

Towards Understanding the Importance of Shortcut Connections in Residual Networks. - Tianyi Liu; Minshuo Chen; Mo Zhou; Simon S. Du; Enlu Zhou; Tuo Zhao (NeurIPS, 2019)

Acceleration via Symplectic Discretization of High-Resolution Differential Equations. - Bin Shi; Simon S. Du; Weijie J. Su; Michael I. Jordan (NeurIPS, 2019)

Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network. - Xiaoxia Wu; Simon S. Du; Rachel A. Ward (CoRR, 2019)

Hitting Time of Stochastic Gradient Langevin Dynamics to Stationary Points: A Direct Analysis. - Xi Chen; Simon S. Du; Xin T. Tong (CoRR, 2019)

Dual Sequential Monte Carlo: Tunneling Filtering and Planning in Continuous POMDPs. - Yunbo Wang; Bo Liu 042; Jiajun Wu 001; Yuke Zhu; Simon S. Du; Li Fei-Fei 001; Joshua B. Tenenbaum (CoRR, 2019)

Continuous Control with Contexts, Provably. - Simon S. Du; Ruosong Wang; Mengdi Wang; Lin F. Yang (CoRR, 2019)

Enhanced Convolutional Neural Tangent Kernels. - Zhiyuan Li 005; Ruosong Wang; Dingli Yu; Simon S. Du; Wei Hu 014; Ruslan Salakhutdinov; Sanjeev Arora (CoRR, 2019)

Gradient Descent for Non-convex Problems in Modern Machine Learning. - Simon S. Du (2019)

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks. - Sanjeev Arora; Simon S. Du; Zhiyuan Li 005; Ruslan Salakhutdinov; Ruosong Wang; Dingli Yu (ICLR, 2020)

Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? - Simon S. Du; Sham M. Kakade; Ruosong Wang; Lin F. Yang (ICLR, 2020)

What Can Neural Networks Reason About? - Keyulu Xu; Jingling Li; Mozhi Zhang; Simon S. Du; Ken-ichi Kawarabayashi; Stefanie Jegelka (ICLR, 2020)

Provable Representation Learning for Imitation Learning via Bi-level Optimization. - Sanjeev Arora; Simon S. Du; Sham M. Kakade; Yuping Luo; Nikunj Saunshi (ICML, 2020)

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs. - Yunbo Wang; Bo Liu 042; Jiajun Wu 001; Yuke Zhu; Simon S. Du; Li Fei-Fei 001; Joshua B. Tenenbaum (IJCAI, 2020)

Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity. - Simon S. Du; Jason D. Lee; Gaurav Mahajan; Ruosong Wang (NeurIPS, 2020)

Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning. - Fei Feng; Ruosong Wang; Wotao Yin; Simon S. Du; Lin F. Yang (NeurIPS, 2020)

Is Long Horizon RL More Difficult Than Short Horizon RL? - Ruosong Wang; Simon S. Du; Lin F. Yang; Sham M. Kakade (NeurIPS, 2020)

On Reward-Free Reinforcement Learning with Linear Function Approximation. - Ruosong Wang; Simon S. Du; Lin F. Yang; Ruslan Salakhutdinov (NeurIPS, 2020)

Planning with General Objective Functions: Going Beyond Total Rewards. - Ruosong Wang; Peilin Zhong; Simon S. Du; Ruslan Salakhutdinov; Lin F. Yang (NeurIPS, 2020)

Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality. - Yi Zhang 074; Orestis Plevrakis; Simon S. Du; Xingguo Li; Zhao Song 002; Sanjeev Arora (NeurIPS, 2020)

Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity. - Simon S. Du; Jason D. Lee; Gaurav Mahajan; Ruosong Wang (CoRR, 2020)

Provably Efficient Exploration for RL with Unsupervised Learning. - Fei Feng; Ruosong Wang; Wotao Yin; Simon S. Du; Lin F. Yang (CoRR, 2020)

Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? - Ruosong Wang; Simon S. Du; Lin F. Yang; Sham M. Kakade (CoRR, 2020)

When is Particle Filtering Efficient for POMDP Sequential Planning? - Simon S. Du; Wei Hu 014; Zhiyuan Li 005; Ruoqi Shen; Zhao Song 002; Jiajun Wu 001 (CoRR, 2020)

Nearly Minimax Optimal Reward-free Reinforcement Learning. - Zihan Zhang; Simon S. Du; Xiangyang Ji (CoRR, 2020)

Provable Benefits of Representation Learning in Linear Bandits. - Jiaqi Yang 001; Wei Hu 014; Jason D. Lee; Simon S. Du (CoRR, 2020)

On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics. - Xi Chen; Simon S. Du; Xin T. Tong (J. Mach. Learn. Res., 2020)

Q-learning with Logarithmic Regret. - Kunhe Yang; Lin F. Yang; Simon S. Du (AISTATS, 2021)

Fine-Grained Gap-Dependent Bounds for Tabular MDPs via Adaptive Multi-Step Bootstrap. - Haike Xu; Tengyu Ma 001; Simon S. Du (COLT, 2021)

Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon. - Zihan Zhang; Xiangyang Ji; Simon S. Du (COLT, 2021)

Optimism in Reinforcement Learning with Generalized Linear Function Approximation. - Yining Wang 001; Ruosong Wang; Simon Shaolei Du; Akshay Krishnamurthy (ICLR, 2021)

Few-Shot Learning via Learning the Representation, Provably. - Simon Shaolei Du; Wei Hu 014; Sham M. Kakade; Jason D. Lee; Qi Lei (ICLR, 2021)

Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization. - Zhenggang Tang; Chao Yu 005; Boyuan Chen 003; Huazhe Xu; Xiaolong Wang 004; Fei Fang 001; Simon Shaolei Du; Yu Wang 002; Yi Wu 013 (ICLR, 2021)

How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. - Keyulu Xu; Mozhi Zhang; Jingling Li; Simon Shaolei Du; Ken-ichi Kawarabayashi; Stefanie Jegelka (ICLR, 2021)

Impact of Representation Learning in Linear Bandits. - Jiaqi Yang 001; Wei Hu 014; Jason D. Lee; Simon Shaolei Du (ICLR, 2021)

Improved Corruption Robust Algorithms for Episodic Reinforcement Learning. - Yifang Chen 001; Simon S. Du; Kevin Jamieson 001 (ICML, 2021)

Bilinear Classes: A Structural Framework for Provable Generalization in RL. - Simon S. Du; Sham M. Kakade; Jason D. Lee; Shachar Lovett; Gaurav Mahajan; Wen Sun 002; Ruosong Wang (ICML, 2021)

On Reinforcement Learning with Adversarial Corruption and Its Application to Block MDP. - Tianhao Wu 002; Yunchang Yang; Simon S. Du; Liwei Wang 001 (ICML, 2021)

Near Optimal Reward-Free Reinforcement Learning. - Zihan Zhang; Simon S. Du; Xiangyang Ji (ICML, 2021)

Corruption Robust Active Learning. - Yifang Chen 001; Simon S. Du; Kevin G. Jamieson (NeurIPS, 2021)

Nearly Horizon-Free Offline Reinforcement Learning. - Tongzheng Ren; Jialian Li; Bo Dai 001; Simon S. Du; Sujay Sanghavi (NeurIPS, 2021)

Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret. - Jean Tarbouriech; Runlong Zhou; Simon S. Du; Matteo Pirotta; Michal Valko; Alessandro Lazaric (NeurIPS, 2021)

Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization. - Tian Ye 007; Simon S. Du (NeurIPS, 2021)

Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP. - Zihan Zhang; Jiaqi Yang 001; Xiangyang Ji; Simon S. Du (NeurIPS, 2021)

When is particle filtering efficient for planning in partially observed linear dynamical systems? - Simon S. Du; Wei Hu 014; Zhiyuan Li 005; Ruoqi Shen; Zhao Song 002; Jiajun Wu 001 (UAI, 2021)

A Provably Efficient Algorithm for Linear Markov Decision Process with Low Switching Cost. - Minbo Gao; Tianle Xie; Simon S. Du; Lin F. Yang (CoRR, 2021)

Variance-Aware Confidence Set: Variance-Dependent Bound for Linear Bandits and Horizon-Free Bound for Linear Mixture MDP. - Zihan Zhang; Jiaqi Yang 001; Xiangyang Ji; Simon S. Du (CoRR, 2021)

Provably Efficient Policy Gradient Methods for Two-Player Zero-Sum Markov Games. - Yulai Zhao 002; Yuandong Tian; Jason D. Lee; Simon S. Du (CoRR, 2021)

Randomized Exploration is Near-Optimal for Tabular MDP. - Zhihan Xiong; Ruoqi Shen; Simon S. Du (CoRR, 2021)

On the Power of Multitask Representation Learning in Linear MDP. - Rui Lu; Gao Huang 001; Simon S. Du (CoRR, 2021)

A Unified Framework for Conservative Exploration. - Yunchang Yang; Tianhao Wu 002; Han Zhong 001; Evrard Garcelon; Matteo Pirotta; Alessandro Lazaric; Liwei Wang 001; Simon S. Du (CoRR, 2021)

Towards Demystifying Representation Learning with Non-contrastive Self-supervision. - Xiang Wang 011; Xinlei Chen; Simon S. Du; Yuandong Tian (CoRR, 2021)

A Benchmark for Low-Switching-Cost Reinforcement Learning. - Shusheng Xu; Yancheng Liang; Yunfei Li 005; Simon Shaolei Du; Yi Wu 013 (CoRR, 2021)

Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels. - Yining Wang 001; Yi Wu 013; Simon S. Du (INFORMS J. Comput., 2021)

AdaLoss: A Computationally-Efficient and Provably Convergent Adaptive Gradient Method. - Xiaoxia Wu; Yuege Xie; Simon Shaolei Du; Rachel A. Ward (AAAI, 2022)

Gap-Dependent Bounds for Two-Player Markov Games. - Zehao Dou; Zhuoran Yang; Zhaoran Wang 001; Simon S. Du (AISTATS, 2022)

Provably Efficient Policy Optimization for Two-Player Zero-Sum Markov Games. - Yulai Zhao 002; Yuandong Tian; Jason D. Lee; Simon S. Du (AISTATS, 2022)

Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies. - Zihan Zhang; Xiangyang Ji; Simon S. Du (COLT, 2022)

Provable Adaptation across Multiway Domains via Representation Learning. - Zhili Feng; Shaobo Han; Simon Shaolei Du (ICLR, 2022)

A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning. - Yunchang Yang; Tianhao Wu 002; Han Zhong 001; Evrard Garcelon; Matteo Pirotta; Alessandro Lazaric; Liwei Wang 001; Simon Shaolei Du (ICLR, 2022)

Denoised MDPs: Learning World Models Better Than the World Itself. - Tongzhou Wang 001; Simon S. Du; Antonio Torralba 001; Phillip Isola; Amy Zhang 001; Yuandong Tian (ICML, 2022)

Near-Optimal Algorithms for Autonomous Exploration and Multi-Goal Stochastic Shortest Path. - Haoyuan Cai; Tengyu Ma 001; Simon S. Du (ICML, 2022)

Active Multi-Task Representation Learning. - Yifang Chen 001; Kevin G. Jamieson; Simon S. Du (ICML, 2022)

First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach. - Andrew J. Wagenmaker; Yifang Chen 001; Max Simchowitz; Simon S. Du; Kevin G. Jamieson (ICML, 2022)

Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes. - Andrew J. Wagenmaker; Yifang Chen 001; Max Simchowitz; Simon S. Du; Kevin G. Jamieson (ICML, 2022)

Nearly Optimal Policy Optimization with Stable at Any Time Guarantee. - Tianhao Wu 002; Yunchang Yang; Han Zhong 001; Liwei Wang 001; Simon S. Du; Jiantao Jiao (ICML, 2022)

Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus. - Qiwen Cui; Simon S. Du (NeurIPS, 2022)

When are Offline Two-Player Zero-Sum Markov Games Solvable? - Qiwen Cui; Simon S. Du (NeurIPS, 2022)

Learning in Congestion Games with Bandit Feedback. - Qiwen Cui; Zhihan Xiong; Maryam Fazel; Simon S. Du (NeurIPS, 2022)

Provable General Function Class Representation Learning in Multitask Bandits and MDP. - Rui Lu; Andrew Zhao; Simon S. Du; Gao Huang 001 (NeurIPS, 2022)

On Gap-dependent Bounds for Offline Reinforcement Learning. - Xinqi Wang; Qiwen Cui; Simon S. Du (NeurIPS, 2022)

Near-Optimal Randomized Exploration for Tabular Markov Decision Processes. - Zhihan Xiong; Ruoqi Shen; Qiwen Cui; Maryam Fazel; Simon S. Du (NeurIPS, 2022)

When is Offline Two-Player Zero-Sum Markov Game Solvable? - Qiwen Cui; Simon S. Du (CoRR, 2022)

TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer. - Meixin Zhu; Simon S. Du; Xuesong Wang 006; Hao (Frank) Yang; Ziyuan Pu; Yinhai Wang (CoRR, 2022)

Understanding Curriculum Learning in Policy Optimization for Solving Combinatorial Optimization Problems. - Runlong Zhou; Yuandong Tian; Yi Wu 013; Simon S. Du (CoRR, 2022)

Nearly Minimax Algorithms for Linear Bandits with Shared Representation. - Jiaqi Yang 001; Qi Lei; Jason D. Lee; Simon S. Du (CoRR, 2022)

Provable General Function Class Representation Learning in Multitask Bandits and MDPs. - Rui Lu; Andrew Zhao; Simon S. Du; Gao Huang 001 (CoRR, 2022)

Optimal Extragradient-Based Bilinearly-Coupled Saddle-Point Optimization. - Simon S. Du; Gauthier Gidel; Michael I. Jordan; Chris Junchi Li (CoRR, 2022)

Horizon-Free Reinforcement Learning for Latent Markov Decision Processes. - Runlong Zhou; Ruosong Wang; Simon S. Du (CoRR, 2022)

Understanding the acceleration phenomenon via high-resolution differential equations. - Bin Shi; Simon S. Du; Michael I. Jordan; Weijie J. Su (Math. Program., 2022)

Blessing of Class Diversity in Pre-training. - Yulai Zhao 002; Jianshu Chen; Simon S. Du (AISTATS, 2023)

Breaking the Curse of Multiagents in a Large State Space: RL in Markov Games with Independent Linear Function Approximation. - Qiwen Cui; Kaiqing Zhang; Simon S. Du (COLT, 2023)

Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron. - Weihang Xu; Simon S. Du (COLT, 2023)

Variance-Aware Sparse Linear Bandits. - Yan Dai 002; Ruosong Wang; Simon Shaolei Du (ICLR, 2023)

Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games. - Shicong Cen; Yuejie Chi; Simon Shaolei Du; Lin Xiao (ICLR, 2023)

Offline Congestion Games: How Feedback Type Affects Data Coverage Requirement. - Haozhe Jiang; Qiwen Cui; Zhihan Xiong; Maryam Fazel; Simon Shaolei Du (ICLR, 2023)

Linear Convergence of Natural Policy Gradient Methods with Log-Linear Policies. - Rui Yuan; Simon Shaolei Du; Robert M. Gower; Alessandro Lazaric; Lin Xiao (ICLR, 2023)

Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing. - Jikai Jin; Zhiyuan Li 005; Kaifeng Lyu; Simon Shaolei Du; Jason D. Lee (ICML, 2023)

Improved Active Multi-Task Representation Learning via Lasso. - Yiping Wang; Yifang Chen 001; Kevin Jamieson 001; Simon Shaolei Du (ICML, 2023)

On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness. - Haotian Ye; Xiaoyu Chen 008; Liwei Wang 001; Simon Shaolei Du (ICML, 2023)

Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes. - Runlong Zhou; Ruosong Wang; Simon Shaolei Du (ICML, 2023)

Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments. - Runlong Zhou; Zihan Zhang; Simon Shaolei Du (ICML, 2023)

Active representation learning for general task space with applications in robotics. - Yifang Chen 001; Yingbing Huang; Simon S. Du; Kevin G. Jamieson; Guanya Shi (NeurIPS, 2023)

Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer. - Yuandong Tian; Yiping Wang; Beidi Chen; Simon S. Du (NeurIPS, 2023)

A Reduction-based Framework for Sequential Decision Making with Delayed Feedback. - Yunchang Yang; Han Zhong 001; Tianhao Wu 002; Bin Liu; Liwei Wang 001; Simon S. Du (NeurIPS, 2023)

Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure. - Angela Yuan; Chris Junchi Li; Gauthier Gidel; Michael I. Jordan; Quanquan Gu; Simon S. Du (NeurIPS, 2023)

LabelBench: A Comprehensive Framework for Benchmarking Label-Efficient Learning. - Jifan Zhang; Yifang Chen 001; Gregory Canal; Stephen Mussmann; Yinglun Zhu; Simon Shaolei Du; Kevin G. Jamieson; Robert D. Nowak (CoRR, 2023)

Beyond Information Gain: An Empirical Benchmark for Low-Switching-Cost Reinforcement Learning. - Shusheng Xu; Yancheng Liang; Yunfei Li 005; Simon Shaolei Du; Yi Wu 013 (Trans. Mach. Learn. Res., 2023)

Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization. - Runlong Zhou; Zelin He; Yuandong Tian; Yi Wu 013; Simon Shaolei Du (Trans. Mach. Learn. Res., 2023)

Integrating the traffic science with representation learning for city-wide network congestion prediction. - Wenqing Zheng; Hao (Frank) Yang; Jiarui Cai; Peihao Wang; Xuan Jiang; Simon Shaolei Du; Yinhai Wang; Zhangyang Wang (Inf. Fusion, 2023)

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models. - Gantavya Bhatt; Yifang Chen 001; Arnav Mohanty Das; Jifan Zhang; Sang T. Truong; Stephen Mussmann; Yinglun Zhu; Jeff A. Bilmes; Simon S. Du; Kevin G. Jamieson; Jordan T. Ash; Robert D. Nowak (ACL (Findings), 2024)

Reflect-RL: Two-Player Online RL Fine-Tuning for LMs. - Runlong Zhou; Simon S. Du; Beibin Li (ACL (1), 2024)

Refined Sample Complexity for Markov Games with Independent Linear Function Approximation (Extended Abstract). - Yan Dai 002; Qiwen Cui; Simon S. Du (COLT, 2024)

Optimal Multi-Distribution Learning. - Zihan Zhang; Wenhao Zhan; Yuxin Chen 002; Simon S. Du; Jason D. Lee (COLT, 2024)

A Black-box Approach for Non-stationary Multi-agent Reinforcement Learning. - Haozhe Jiang; Qiwen Cui; Zhihan Xiong; Maryam Fazel; Simon Shaolei Du (ICLR, 2024)

Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking. - Kaifeng Lyu; Jikai Jin; Zhiyuan Li 005; Simon Shaolei Du; Jason D. Lee; Wei Hu 014 (ICLR, 2024)

Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning. - Ruizhe Shi; Yuyao Liu; Yanjie Ze; Simon Shaolei Du; Huazhe Xu (ICLR, 2024)

JoMA: Demystifying Multilayer Transformers via Joint Dynamics of MLP and Attention. - Yuandong Tian; Yiping Wang; Zhenyu Zhang 015; Beidi Chen; Simon Shaolei Du (ICLR, 2024)

How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization. - Nuoya Xiong; Lijun Ding; Simon Shaolei Du (ICLR, 2024)

Horizon-Free Regret for Linear Markov Decision Processes. - Zihan Zhang; Jason D. Lee; Yuxin Chen 002; Simon Shaolei Du (ICLR, 2024)

Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning. - Zhaoyi Zhou; Chuning Zhu; Runlong Zhou; Qiwen Cui; Abhishek Gupta 004; Simon Shaolei Du (ICLR, 2024)

Rethinking Transformers in Solving POMDPs. - Chenhao Lu; Ruizhe Shi; Yuyao Liu; Kaizhe Hu; Simon Shaolei Du; Huazhe Xu (ICML, 2024)

Learning Optimal Tax Design in Nonatomic Congestion Games. - Qiwen Cui; Maryam Fazel; Simon S. Du (NeurIPS, 2024)

Learning to Cooperate with Humans using Generative Agents. - Yancheng Liang; Daphne Chen; Abhishek Gupta 004; Simon S. Du; Natasha Jaques (NeurIPS, 2024)

Understanding the Gains from Repeated Self-Distillation. - Divyansh Pareek; Simon S. Du; Sewoong Oh (NeurIPS, 2024)

Decoding-Time Language Model Alignment with Multiple Objectives. - Ruizhe Shi; Yifang Chen 001; Yushi Hu; Alisa Liu; Hanna Hajishirzi; Noah A. Smith; Simon S. Du (NeurIPS, 2024)

CLIPLoss and Norm-Based Data Selection Methods for Multimodal Contrastive Learning. - Yiping Wang; Yifang Chen 001; Wendan Yan; Alex Fang; Wenjing Zhou; Kevin G. Jamieson; Simon S. Du (NeurIPS, 2024)

Toward Global Convergence of Gradient EM for Over-Paramterized Gaussian Mixture Models. - Weihang Xu; Maryam Fazel; Simon S. Du (NeurIPS, 2024)

Distributional Successor Features Enable Zero-Shot Policy Optimization. - Chuning Zhu; Xinqi Wang; Tyler Han; Simon S. Du; Abhishek Gupta 004 (NeurIPS, 2024)

Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning. - Yiping Wang; Yifang Chen 001; Wendan Yan; Kevin G. Jamieson; Simon Shaolei Du (CoRR, 2024)

Refined Sample Complexity for Markov Games with Independent Linear Function Approximation. - Yan Dai 002; Qiwen Cui; Simon S. Du (CoRR, 2024)

Transferable Reinforcement Learning via Generalized Occupancy Models. - Chuning Zhu; Xinqi Wang; Tyler Han; Simon S. Du; Abhishek Gupta 004 (CoRR, 2024)

Toward Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixture Models. - Weihang Xu; Maryam Fazel; Simon S. Du (CoRR, 2024)

Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning. - Yifang Chen 001; Shuohang Wang; Ziyi Yang; Hiteshi Sharma; Nikos Karampatziakis; Donghan Yu; Kevin G. Jamieson; Simon Shaolei Du; Yelong Shen (CoRR, 2024)

Multi-Agent Reinforcement Learning from Human Feedback: Data Coverage and Algorithmic Techniques. - Natalia Zhang; Xinqi Wang; Qiwen Cui; Runlong Zhou; Sham M. Kakade; Simon S. Du (CoRR, 2024)

Transformers are Efficient Compilers, Provably. - Xiyu Zhai; Runlong Zhou; Liao Zhang; Simon Shaolei Du (CoRR, 2024)

On Erroneous Agreements of CLIP Image Embeddings. - Siting Li; Pang Wei Koh; Simon Shaolei Du (CoRR, 2024)

Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration. - Avinandan Bose; Zhihan Xiong; Aadirupa Saha; Simon Shaolei Du; Maryam Fazel (CoRR, 2024)

Exploring How Generative MLLMs Perceive More Than CLIP with the Same Vision Encoder. - Siting Li; Pang Wei Koh; Simon Shaolei Du (ACL (1), 2025)

Offline Multi-task Transfer RL with Representational Penalization. - Avinandan Bose; Simon Shaolei Du; Maryam Fazel (AISTATS, 2025)

Anytime Acceleration of Gradient Descent. - Zihan Zhang; Jason D. Lee; Simon S. Du; Yuxin Chen 002 (COLT, 2025)

Is Your World Simulator a Good Story Presenter? A Consecutive Events-Based Benchmark for Future Long Video Generation. - Yiping Wang; Xuehai He; Kuan Wang; Luyao Ma; Jianwei Yang; Shuohang Wang; Simon Shaolei Du; Yelong Shen (CVPR, 2025)

The Crucial Role of Samplers in Online Direct Preference Optimization. - Ruizhe Shi; Runlong Zhou; Simon Shaolei Du (ICLR, 2025)

SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters. - Yiping Wang; Hanxian Huang; Yifang Chen 001; Jishen Zhao; Simon Shaolei Du; Yuandong Tian (CoRR, 2025)

Towards Understanding the Benefit of Multitask Representation Learning in Decision Process. - Rui Lu; Yang Yue; Andrew Zhao; Simon S. Du; Gao Huang 001 (CoRR, 2025)

A Minimalist Example of Edge-of-Stability and Progressive Sharpening. - Liming Liu; Zixuan Zhang; Simon S. Du; Tuo Zhao (CoRR, 2025)

Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback. - Runlong Zhou; Maryam Fazel; Simon S. Du (CoRR, 2025)

Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination. - Kunal Jha; Wilka Carvalho; Yancheng Liang; Simon S. Du; Max Kleiman-Weiner; Natasha Jaques (CoRR, 2025)

LoRe: Personalizing LLMs via Low-Rank Reward Modeling. - Avinandan Bose; Zhihan Xiong; Yuejie Chi; Simon Shaolei Du; Lin Xiao; Maryam Fazel (CoRR, 2025)

Improving Human-AI Coordination through Adversarial Training and Generative Models. - Paresh Chaudhary; Yancheng Liang; Daphne Chen; Simon S. Du; Natasha Jaques (CoRR, 2025)

Reinforcement Learning for Reasoning in Large Language Models with One Training Example. - Yiping Wang; Qing Yang; Zhiyuan Zeng; Liliang Ren; Lucas Liu; Baolin Peng; Hao Cheng 002; Xuehai He; Kuan Wang; Jianfeng Gao 001; Weizhu Chen; Shuohang Wang; et al. (CoRR, 2025)

Highlighting What Matters: Promptable Embeddings for Attribute-Focused Image Retrieval. - Siting Li; Xiang Gao; Simon Shaolei Du (CoRR, 2025)

Understanding the Performance Gap in Preference Learning: A Dichotomy of RLHF and DPO. - Ruizhe Shi; Minhak Song; Runlong Zhou; Zihan Zhang; Maryam Fazel; Simon S. Du (CoRR, 2025)

Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs. - Shulun Chen; Runlong Zhou; Zihan Zhang; Maryam Fazel; Simon S. Du (CoRR, 2025)

Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures. - Mo Zhou; Weihang Xu; Maryam Fazel; Simon S. Du (CoRR, 2025)

Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models. - Mickel Liu; Liwei Jiang; Yancheng Liang; Simon Shaolei Du; Yejin Choi 001; Tim Althoff; Natasha Jaques (CoRR, 2025)

Policy-Based Trajectory Clustering in Offline Reinforcement Learning. - Hao Hu; Xinqi Wang; Simon Shaolei Du (CoRR, 2025)

Spurious Rewards: Rethinking Training Signals in RLVR. - Rulin Shao; Shuyue Stella Li; Rui Xin; Scott Geng; Yiping Wang; Sewoong Oh; Simon Shaolei Du; Nathan Lambert 001; Sewon Min; Ranjay Krishna; Yulia Tsvetkov; Hannaneh Hajishirzi; et al. (CoRR, 2025)

Settling the Sample Complexity of Online Reinforcement Learning. - Zihan Zhang; Yuxin Chen 002; Jason D. Lee; Simon S. Du (J. ACM, 2025)