← Authors

Quanquan Gu

193 papers · 2012–2025 · 14 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌍 Conference Polyglot (14) πŸ—ΊοΈ Taxonomy Completionist (25) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (7) πŸƒ Academic Marathon (13) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🏠 Conference Loyalist (52) 🌟 Keyword Trendsetter Combo (5) 🀝 Dynamic Duo (36) πŸ‘‘ Triple Crown πŸ† Keyword Champion (5) πŸ† Grand Slam πŸ‘₯ Mega-Team (71) πŸ”¬ Deep Specialist (46) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (14) ❓ The Questioner (5) πŸ’Ž Century Club (193) πŸ—ƒοΈ Keyword Collector (113) ⚑ Prolific Year (29)

Conferences

ICML (62) NIPS (52) ICLR (29) AISTATS (22) UAI (6) AAAI (5) COLT (5) IJCAI (3) JMLR (3) ALT (2) ACML (1) CVPR (1) EMNLP (1) NAACL (1)

Top co-authors

Dongruo Zhou (36) Pan Xu (28) Difan Zou (25) Jiafan He (23) Zixiang Chen (23) Yuan Cao (21) Lingxiao Wang (15) Yue Wu (14) Weitong ZHANG (11) Jingfeng Wu (10)

Research topics

Reinforcement Learning (1) Differential Privacy (1) Statistics (1)

Keywords

regret bound (24) nonconvex optimization (18) gradient descent (17) reinforcement learning (15) stochastic gradient descent (14) variance reduction (13) sample complexity (12) linear function approximation (11) function approximation (8) markov decision process (8) stochastic optimization (7) neural network (7) stochastic gradient (7) convergence rate (7) adversarial robustness (7) linear mixture mdp (7) neural network optimization (6) adversarial training (6) learning theory (6) multi-armed bandit (5)

Papers

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