Quanquan Gu

Avoiding exp($k^*$) Scaling for Thompson Sampling in Combinatorial Semi-Bandits: From Multiple Seeds to a Single Seed COLT 2026 An All-Atom Generative Model for Designing Protein Complexes ICML 2025 Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignment ICML 2025 Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration ICLR 2025 Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis ICLR 2025 CryoFM: A Flow-based Foundation Model for Cryo-EM Densities ICLR 2025 Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling ICML 2025 DPLM-2: A Multimodal Diffusion Protein Language Model ICLR 2025 Elucidating the Design Space of Multimodal Protein Language Models ICML 2025 Energy-Weighted Flow Matching for Offline Reinforcement Learning ICLR 2025 Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μ Parametrization ICML 2025 LLaVA-Critic: Learning to Evaluate Multimodal Models CVPR 2025 Logarithmic Regret for Online KL-Regularized Reinforcement Learning ICML 2025 MARS: Unleashing the Power of Variance Reduction for Training Large Models ICML 2025 Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance ICML 2025 Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback ICML 2025 On the Power of Multitask Representation Learning with Gradient Descent AISTATS 2025 ProteinBench: A Holistic Evaluation of Protein Foundation Models ICLR 2025 Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity ICML 2025 Self-Play Preference Optimization for Language Model Alignment ICLR 2025 Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers ICLR 2025 Achieving Constant Regret in Linear Markov Decision Processes NeurIPS 2024 A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation NeurIPS 2024 Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization NeurIPS 2024 Borda Regret Minimization for Generalized Linear Dueling Bandits ICML 2024 DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization ICLR 2024 Diffusion Language Models Are Versatile Protein Learners ICML 2024 Enhancing Large Vision Language Models with Self-Training on Image Comprehension NeurIPS 2024 Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time NeurIPS 2024 Feel-Good Thompson Sampling for Contextual Dueling Bandits ICML 2024 Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs ICLR 2024 How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR 2024 Large Language Models Can Be Contextual Privacy Protection Learners EMNLP 2024 Matching the Statistical Query Lower Bound for k-Sparse Parity Problems with Sign Stochastic Gradient Descent NeurIPS 2024 Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning ICLR 2024 Position: TrustLLM: Trustworthiness in Large Language Models ICML 2024 Protein Conformation Generation via Force-Guided SE(3) Diffusion Models ICML 2024 Pure Exploration in Asynchronous Federated Bandits UAI 2024 Risk Bounds of Accelerated SGD for Overparameterized Linear Regression ICLR 2024 Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models ICML 2024 Self-Play Fine-tuning of Diffusion Models for Text-to-image Generation NeurIPS 2024 Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption ICML 2024 Uncertainty-Aware Reward-Free Exploration with General Function Approximation ICML 2024 Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP ICLR 2024 Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits ICLR 2024 A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning ICLR 2023 Benign Overfitting in Adversarially Robust Linear Classification UAI 2023 Benign Overfitting in Two-layer ReLU Convolutional Neural Networks ICML 2023 Benign Overfitting of Constant-Stepsize SGD for Linear Regression JMLR 2023 Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation ICML 2023 Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes ICML 2023 Corruption-Robust Offline Reinforcement Learning with General Function Approximation NeurIPS 2023 DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design ICML 2023 Efficient Privacy-Preserving Stochastic Nonconvex Optimization UAI 2023 Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron ICML 2023 How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study ICLR 2023 Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data NeurIPS 2023 Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path ICML 2023 Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes ICML 2023 Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization ICML 2023 On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits ICML 2023 Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure NeurIPS 2023 Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs ICML 2023 Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits ICML 2023 Personalized Federated Learning under Mixture of Distributions ICML 2023 Robust Learning with Progressive Data Expansion Against Spurious Correlation NeurIPS 2023 Structure-informed Language Models Are Protein Designers ICML 2023 The Benefits of Mixup for Feature Learning ICML 2023 The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks COLT 2023 Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization ICLR 2023 Understanding Train-Validation Split in Meta-Learning with Neural Networks ICLR 2023 Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension UAI 2023 Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency COLT 2023 Why Does Sharpness-Aware Minimization Generalize Better Than SGD? NeurIPS 2023 Active Ranking without Strong Stochastic Transitivity NeurIPS 2022 Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons AISTATS 2022 Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games ALT 2022 A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits NeurIPS 2022 Benign Overfitting in Two-layer Convolutional Neural Networks NeurIPS 2022 Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs NeurIPS 2022 Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization ICML 2022 Efficient Robust Training via Backward Smoothing AAAI 2022 Faster Perturbed Stochastic Gradient Methods for Finding Local Minima ALT 2022 Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression ICML 2022 Learning Neural Contextual Bandits through Perturbed Rewards ICLR 2022 Learning Stochastic Shortest Path with Linear Function Approximation ICML 2022 Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium NeurIPS 2022 Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes ACML 2022 Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation AISTATS 2022 Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions NeurIPS 2022 Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs AISTATS 2022 Neural Contextual Bandits with Deep Representation and Shallow Exploration ICLR 2022 On the Convergence of Certified Robust Training with Interval Bound Propagation ICLR 2022 On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs ICML 2022 Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime NeurIPS 2022 Self-training Converts Weak Learners to Strong Learners in Mixture Models AISTATS 2022 The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift NeurIPS 2022 Towards Understanding the Mixture-of-Experts Layer in Deep Learning NeurIPS 2022 Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins ICML 2021 Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits ICML 2021 Benign Overfitting of Constant-Stepsize SGD for Linear Regression COLT 2021 Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate ICLR 2021 Double Explore-then-Commit: Asymptotic Optimality and Beyond COLT 2021 Do Wider Neural Networks Really Help Adversarial Robustness? NeurIPS 2021 Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks NeurIPS 2021 Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling UAI 2021 How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? ICLR 2021 Iterative Teacher-Aware Learning NeurIPS 2021 Logarithmic Regret for Reinforcement Learning with Linear Function Approximation ICML 2021 MOTS: Minimax Optimal Thompson Sampling ICML 2021 Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs NeurIPS 2021 Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes COLT 2021 Neural Thompson Sampling ICLR 2021 On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients ICML 2021 Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise ICML 2021 Provable Robustness of Adversarial Training for Learning Halfspaces with Noise ICML 2021 Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping ICML 2021 Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints NeurIPS 2021 Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent NeurIPS 2021 Pure Exploration in Kernel and Neural Bandits NeurIPS 2021 Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation NeurIPS 2021 Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures NeurIPS 2021 The Benefits of Implicit Regularization from SGD in Least Squares Problems NeurIPS 2021 Towards Understanding the Spectral Bias of Deep Learning IJCAI 2021 Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation NeurIPS 2021 Variance-Aware Off-Policy Evaluation with Linear Function Approximation NeurIPS 2021 Variance-reduced First-order Meta-learning for Natural Language Processing Tasks NAACL 2021 Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization AISTATS 2020 A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation ICML 2020 A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods NeurIPS 2020 A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks AAAI 2020 A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks NeurIPS 2020 Agnostic Learning of a Single Neuron with Gradient Descent NeurIPS 2020 A Knowledge Transfer Framework for Differentially Private Sparse Learning AAAI 2020 Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks IJCAI 2020 Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks AAAI 2020 Improving Adversarial Robustness Requires Revisiting Misclassified Examples ICLR 2020 Improving Neural Language Generation with Spectrum Control ICLR 2020 Neural Contextual Bandits with UCB-based Exploration ICML 2020 On the Global Convergence of Training Deep Linear ResNets ICLR 2020 Optimization Theory for ReLU Neural Networks Trained with Normalization Layers ICML 2020 Rank Aggregation via Heterogeneous Thurstone Preference Models AAAI 2020 Sample Efficient Policy Gradient Methods with Recursive Variance Reduction ICLR 2020 Stochastic Nested Variance Reduction for Nonconvex Optimization JMLR 2020 Stochastic Recursive Variance-Reduced Cubic Regularization Methods AISTATS 2020 Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models AISTATS 2020 Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks NeurIPS 2019 An Improved Analysis of Training Over-parameterized Deep Neural Networks NeurIPS 2019 An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient UAI 2019 Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning IJCAI 2019 Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks NeurIPS 2019 Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks NeurIPS 2019 Learning One-hidden-layer ReLU Networks via Gradient Descent AISTATS 2019 Lower Bounds for Smooth Nonconvex Finite-Sum Optimization ICML 2019 On the Convergence and Robustness of Adversarial Training ICML 2019 Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics AISTATS 2019 Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction NeurIPS 2019 Stochastic Variance-Reduced Cubic Regularization Methods JMLR 2019 Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks NeurIPS 2019 Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms AISTATS 2018 A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery ICML 2018 A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery AISTATS 2018 Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions ICML 2018 Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization ICML 2018 Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization NeurIPS 2018 Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow ICML 2018 Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization NeurIPS 2018 Stochastic Nested Variance Reduction for Nonconvex Optimization NeurIPS 2018 Stochastic Variance-Reduced Cubic Regularized Newton Methods ICML 2018 Stochastic Variance-Reduced Hamilton Monte Carlo Methods ICML 2018 Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima NeurIPS 2018 A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation AISTATS 2017 A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery ICML 2017 Communication-efficient Distributed Sparse Linear Discriminant Analysis AISTATS 2017 Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent AISTATS 2017 High-dimensional Time Series Clustering via Cross-Predictability AISTATS 2017 High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm ICML 2017 Robust Gaussian Graphical Model Estimation with Arbitrary Corruption ICML 2017 Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization NeurIPS 2017 Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference ICML 2017 Low-Rank and Sparse Structure Pursuit via Alternating Minimization AISTATS 2016 On the Statistical Limits of Convex Relaxations ICML 2016 Optimal Statistical and Computational Rates for One Bit Matrix Completion AISTATS 2016 Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates AISTATS 2016 Semiparametric Differential Graph Models NeurIPS 2016 Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation ICML 2016 High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality NeurIPS 2015 Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing ICML 2015 Robust Tensor Decomposition with Gross Corruption NeurIPS 2014 Sparse PCA with Oracle Property NeurIPS 2014 Clustered Support Vector Machines AISTATS 2013 Unsupervised Link Selection in Networks AISTATS 2013 Locality Preserving Feature Learning AISTATS 2012 Selective Labeling via Error Bound Minimization NeurIPS 2012