A novel similarity measure under Riemannian metric for stereo matching. - Quanquan Gu; Jie Zhou 001 (ICASSP, 2008)

Belief propagation on Riemannian manifold for stereo matching. - Quanquan Gu; Jie Zhou 001 (ICIP, 2008)

A similarity measure under Log-Euclidean metric for stereo matching. - Quanquan Gu; Jie Zhou 001 (ICPR, 2008)

Neighborhood Preserving Nonnegative Matrix Factorization. - Quanquan Gu; Jie Zhou 001 (BMVC, 2009)

Multiframe Motion Segmentation via Penalized MAP Estimation and Linear Programming. - Han Hu 001; Quanquan Gu; Lei Deng; Jie Zhou 001 (BMVC, 2009)

Subspace maximum margin clustering. - Quanquan Gu; Jie Zhou 001 (CIKM, 2009)

Regular simplex criterion: A novel feature extraction criterion. - Quanquan Gu; Jie Zhou 001 (ICASSP, 2009)

Two dimensional Maximum Margin Criterion. - Quanquan Gu; Jie Zhou 001 (ICASSP, 2009)

Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification. - Quanquan Gu; Jie Zhou 001 (ICDM, 2009)

Multiple Kernel Maximum Margin Criterion. - Quanquan Gu; Jie Zhou 001 (ICIP, 2009)

Two Dimensional Nonnegative Matrix Factorization. - Quanquan Gu; Jie Zhou 001 (ICIP, 2009)

Local Learning Regularized Nonnegative Matrix Factorization. - Quanquan Gu; Jie Zhou 001 (IJCAI, 2009)

Co-clustering on manifolds. - Quanquan Gu; Jie Zhou 001 (KDD, 2009)

Transductive Classification via Dual Regularization. - Quanquan Gu; Jie Zhou 001 (ECML/PKDD (1), 2009)

Local Relevance Weighted Maximum Margin Criterion for Text Classification. - Quanquan Gu; Jie Zhou 001 (SDM, 2009)

HTF: a novel feature for general crack detection. - Han Hu 001; Quanquan Gu; Jie Zhou 001 (ICIP, 2010)

Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs. - Quanquan Gu; Jie Zhou 001; Chris H. Q. Ding (SDM, 2010)

Learning a Kernel for Multi-Task Clustering. - Quanquan Gu; Zhenhui Li; Jiawei Han 001 (AAAI, 2011)

Towards feature selection in network. - Quanquan Gu; Jiawei Han 001 (CIKM, 2011)

Correlated multi-label feature selection. - Quanquan Gu; Zhenhui Li; Jiawei Han 001 (CIKM, 2011)

On Trivial Solution and Scale Transfer Problems in Graph Regularized NMF. - Quanquan Gu; Chris H. Q. Ding; Jiawei Han 001 (IJCAI, 2011)

Joint Feature Selection and Subspace Learning. - Quanquan Gu; Zhenhui Li; Jiawei Han 001 (IJCAI, 2011)

Linear Discriminant Dimensionality Reduction. - Quanquan Gu; Zhenhui Li; Jiawei Han 001 (ECML/PKDD (1), 2011)

Generalized Fisher Score for Feature Selection. - Quanquan Gu; Zhenhui Li; Jiawei Han 001 (UAI, 2011)

Towards Active Learning on Graphs: An Error Bound Minimization Approach. - Quanquan Gu; Jiawei Han 001 (ICDM, 2012)

Selective Labeling via Error Bound Minimization. - Quanquan Gu; Tong Zhang 001; Chris H. Q. Ding; Jiawei Han 001 (NIPS, 2012)

IntruMine: Mining Intruders in Untrustworthy Data of Cyber-physical Systems. - Lu-An Tang; Quanquan Gu; Xiao Yu 007; Jiawei Han 001; Thomas La Porta; Alice Leung; Tarek F. Abdelzaher; Lance M. Kaplan (SDM, 2012)

Citation Prediction in Heterogeneous Bibliographic Networks. - Xiao Yu 007; Quanquan Gu; Mianwei Zhou; Jiawei Han 001 (SDM, 2012)

Locality Preserving Feature Learning. - Quanquan Gu; Marina Danilevsky; Zhenhui Li; Jiawei Han 001 (AISTATS, 2012)

Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling. - Zhijun Yin; Liangliang Cao; Quanquan Gu; Jiawei Han 001 (ACM Trans. Intell. Syst. Technol., 2012)

Unsupervised Link Selection in Networks. - Quanquan Gu; Charu C. Aggarwal; Jiawei Han 001 (AISTATS, 2013)

Clustered Support Vector Machines. - Quanquan Gu; Jiawei Han 001 (AISTATS, 2013)

Wireless sensor network data collection by connected cooperative UAVs. - Peng Wei; Quanquan Gu; Dengfeng Sun (ACC, 2013)

Selective sampling on graphs for classification. - Quanquan Gu; Charu C. Aggarwal; Jialu Liu; Jiawei Han 001 (KDD, 2013)

Mining lines in the sand: on trajectory discovery from untrustworthy data in cyber-physical system. - Lu-An Tang; Xiao Yu 007; Quanquan Gu; Jiawei Han 001; Alice Leung; Thomas La Porta (KDD, 2013)

Recommendation in heterogeneous information networks with implicit user feedback. - Xiao Yu 007; Xiang Ren 001; Yizhou Sun; Bradley Sturt; Urvashi Khandelwal; Quanquan Gu; Brandon Norick; Jiawei Han 001 (RecSys, 2013)

Trustworthiness analysis of sensor data in cyber-physical systems. - Lu-An Tang; Xiao Yu 007; Sangkyum Kim; Quanquan Gu; Jiawei Han 001; Alice Leung; Thomas La Porta (J. Comput. Syst. Sci., 2013)

Active Learning: A Survey. - Charu C. Aggarwal; Xiangnan Kong; Quanquan Gu; Jiawei Han 001; Philip S. Yu (Data Classification: Algorithms and Applications, 2014)

Online Spectral Learning on a Graph with Bandit Feedback. - Quanquan Gu; Jiawei Han 001 (ICDM, 2014)

ClusCite: effective citation recommendation by information network-based clustering. - Xiang Ren 001; Jialu Liu; Xiao Yu 007; Urvashi Khandelwal; Quanquan Gu; Lidan Wang; Jiawei Han 001 (KDD, 2014)

Robust Tensor Decomposition with Gross Corruption. - Quanquan Gu; Huan Gui; Jiawei Han 001 (NIPS, 2014)

Sparse PCA with Oracle Property. - Quanquan Gu; Zhaoran Wang 001; Han Liu 001 (NIPS, 2014)

Batch-Mode Active Learning via Error Bound Minimization. - Quanquan Gu; Tong Zhang 001; Jiawei Han 001 (UAI, 2014)

Personalized entity recommendation: a heterogeneous information network approach. - Xiao Yu 007; Xiang Ren 001; Yizhou Sun; Quanquan Gu; Bradley Sturt; Urvashi Khandelwal; Brandon Norick; Jiawei Han 001 (WSDM, 2014)

A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression. - Yiyi Liu; Quanquan Gu; Jack P. Hou; Jiawei Han 001; Jian Ma 004 (BMC Bioinform., 2014)

Online and active learning of big networks: theory and algorithms - Quanquan Gu (2014)

Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks. - Chang Wan; Xiang Li 067; Ben Kao; Xiao Yu 007; Quanquan Gu; David Wai-Lok Cheung; Jiawei Han 001 (CIKM, 2015)

Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing. - Rongda Zhu; Quanquan Gu (ICML, 2015)

High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality. - Zhaoran Wang 001; Quanquan Gu; Yang Ning; Han Liu 001 (NIPS, 2015)

Robust Classification of Information Networks by Consistent Graph Learning. - Shi Zhi; Jiawei Han 001; Quanquan Gu (ECML/PKDD (2), 2015)

GIN: A Clustering Model for Capturing Dual Heterogeneity in Networked Data. - Jialu Liu; Chi Wang 001; Jing Gao 004; Quanquan Gu; Charu C. Aggarwal; Lance M. Kaplan; Jiawei Han 001 (SDM, 2015)

A Framework of Mining Trajectories from Untrustworthy Data in Cyber-Physical System. - Lu-An Tang; Xiao Yu 007; Quanquan Gu; Jiawei Han 001; Guofei Jiang; Alice Leung; Thomas La Porta (ACM Trans. Knowl. Discov. Data, 2015)

Low-Rank and Sparse Structure Pursuit via Alternating Minimization. - Quanquan Gu; Zhaoran Wang 001; Han Liu 001 (AISTATS, 2016)

Optimal Statistical and Computational Rates for One Bit Matrix Completion. - Renkun Ni; Quanquan Gu (AISTATS, 2016)

Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates. - Lingxiao Wang 001; Xiang Ren 001; Quanquan Gu (AISTATS, 2016)

Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation. - Huan Gui; Jiawei Han 001; Quanquan Gu (ICML, 2016)

On the Statistical Limits of Convex Relaxations. - Zhaoran Wang 001; Quanquan Gu; Han Liu 001 (ICML, 2016)

Accelerated Stochastic Block Coordinate Descent with Optimal Sampling. - Aston Zhang; Quanquan Gu (KDD, 2016)

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

Contextual Bandits in a Collaborative Environment. - Qingyun Wu; Huazheng Wang; Quanquan Gu; Hongning Wang (SIGIR, 2016)

Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization. - Jinghui Chen; Quanquan Gu (UAI, 2016)

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

High-dimensional Time Series Clustering via Cross-Predictability. - Dezhi Hong; Quanquan Gu; Kamin Whitehouse (AISTATS, 2017)

Communication-efficient Distributed Sparse Linear Discriminant Analysis. - Lu Tian; Quanquan Gu (AISTATS, 2017)

A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation. - Lingxiao Wang 001; Xiao Zhang 016; Quanquan Gu (AISTATS, 2017)

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)

Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. - Lingxiao Wang 001; Quanquan Gu (ICML, 2017)

A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. - Lingxiao Wang 001; Xiao Zhang 016; Quanquan Gu (ICML, 2017)

High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. - Rongda Zhu; Lingxiao Wang 001; Chengxiang Zhai; Quanquan Gu (ICML, 2017)

Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization. - Jinghui Chen; Quanquan Gu (KDD, 2017)

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

Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption. - Jinghui Chen; Lingxiao Wang 001; Xiao Zhang 016; Quanquan Gu (CoRR, 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)

Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently. - Yaodong Yu; Difan Zou; 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)

A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. - Xiao Zhang 016; Lingxiao Wang 001; Quanquan Gu (AISTATS, 2018)

Towards Personalized Learning in Mobile Sensing Systems. - Wenjun Jiang; Qi Li 012; Lu Su 001; Chenglin Miao; Quanquan Gu; Wenyao Xu (ICDCS, 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)

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

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. - Xiao Zhang 016; Lingxiao Wang 001; Yaodong Yu; 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)

Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization. - Bargav Jayaraman; Lingxiao Wang 001; David Evans 001; Quanquan Gu (NeurIPS, 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)

Differentially Private Hypothesis Transfer Learning. - Yang Wang; Quanquan Gu; Donald E. Brown (ECML/PKDD (2), 2018)

Continuous-Trait Probabilistic Model for Comparing Multi-species Functional Genomic Data. - Yang Yang 094; Quanquan Gu; Takayo Sasaki; Julianna Crivello; Rachel O'Neill; David M. Gilbert; Jian Ma 004 (RECOMB, 2018)

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

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. - Jinghui Chen; Quanquan Gu (CoRR, 2018)

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

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. - Dongruo Zhou; Yiqi Tang; Ziyan Yang; Yuan Cao 006; Quanquan Gu (CoRR, 2018)

Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. - Difan Zou; Yuan Cao 006; Dongruo Zhou; Quanquan Gu (CoRR, 2018)

A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. - Jinghui Chen; Jinfeng Yi; Quanquan Gu (CoRR, 2018)

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

Learning One-hidden-layer ReLU Networks via Gradient Descent. - Xiao Zhang 016; Yaodong Yu; Lingxiao Wang 001; Quanquan Gu (AISTATS, 2019)

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

On the Convergence and Robustness of Adversarial Training. - Yisen Wang 001; Xingjun Ma; James Bailey 001; Jinfeng Yi; Bowen Zhou 001; Quanquan Gu (ICML, 2019)

Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. - Dongruo Zhou; Quanquan Gu (ICML, 2019)

Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning. - Lingxiao Wang 001; Quanquan Gu (IJCAI, 2019)

Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. - Yuan Cao 006; Quanquan Gu (NeurIPS, 2019)

Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. - Yuan Cao 006; Quanquan Gu (NeurIPS, 2019)

Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. - Spencer Frei; Yuan Cao 006; Quanquan Gu (NeurIPS, 2019)

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

An Improved Analysis of Training Over-parameterized Deep Neural Networks. - Difan Zou; Quanquan Gu (NeurIPS, 2019)

Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. - Difan Zou; Ziniu Hu; Yewen Wang; Song Jiang 002; Yizhou Sun; Quanquan Gu (NeurIPS, 2019)

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

A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks. - Yuan Cao 006; Quanquan Gu (CoRR, 2019)

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. - Bao Wang 001; Quanquan Gu; March Boedihardjo; Farzin Barekat; Stanley J. Osher (CoRR, 2019)

Efficient Privacy-Preserving Nonconvex Optimization. - Lingxiao Wang 001; Bargav Jayaraman; David Evans 001; Quanquan Gu (CoRR, 2019)

Neural Contextual Bandits with Upper Confidence Bound-Based Exploration. - Dongruo Zhou; Lihong Li 001; Quanquan Gu (CoRR, 2019)

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

Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks. - Yuan Cao 006; Quanquan Gu (AAAI, 2020)

A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. - Jinghui Chen; Dongruo Zhou; Jinfeng Yi; Quanquan Gu (AAAI, 2020)

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

A Knowledge Transfer Framework for Differentially Private Sparse Learning. - Lingxiao Wang 001; Quanquan Gu (AAAI, 2020)

Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models. - Xiao Zhang 016; Jinghui Chen; Quanquan Gu; David Evans 001 (AISTATS, 2020)

Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. - Dongruo Zhou; Yuan Cao 006; Quanquan Gu (AISTATS, 2020)

Stochastic Recursive Variance-Reduced Cubic Regularization Methods. - Dongruo Zhou; Quanquan Gu (AISTATS, 2020)

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

Improving Adversarial Robustness Requires Revisiting Misclassified Examples. - Yisen Wang 001; Difan Zou; Jinfeng Yi; James Bailey 001; Xingjun Ma; Quanquan Gu (ICLR, 2020)

Improving Neural Language Generation with Spectrum Control. - Lingxiao Wang 001; Jing Huang 019; Kevin Huang 002; Ziniu Hu; Guangtao Wang; Quanquan Gu (ICLR, 2020)

On the Global Convergence of Training Deep Linear ResNets. - Difan Zou; Philip M. Long; Quanquan Gu (ICLR, 2020)

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

Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. - Yonatan Dukler; Quanquan Gu; Guido Montúfar (ICML, 2020)

Neural Contextual Bandits with UCB-based Exploration. - Dongruo Zhou; Lihong Li 001; Quanquan Gu (ICML, 2020)

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. - Jinghui Chen; Dongruo Zhou; Yiqi Tang; Ziyan Yang; Yuan Cao 006; Quanquan Gu (IJCAI, 2020)

RayS: A Ray Searching Method for Hard-label Adversarial Attack. - Jinghui Chen; Quanquan Gu (KDD, 2020)

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. - Bao Wang 001; Quanquan Gu; March Boedihardjo; Lingxiao Wang 001; Farzin Barekat; Stanley J. Osher (MSML, 2020)

A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. - Zixiang Chen; Yuan Cao 006; Quanquan Gu; Tong Zhang 001 (NeurIPS, 2020)

Agnostic Learning of a Single Neuron with Gradient Descent. - Spencer Frei; Yuan Cao 006; Quanquan Gu (NeurIPS, 2020)

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

Is neuron coverage a meaningful measure for testing deep neural networks? - Fabrice Harel-Canada; Lingxiao Wang 001; Muhammad Ali Gulzar; Quanquan Gu; Miryung Kim (ESEC/SIGSOFT FSE, 2020)

Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. - Zixiang Chen; Yuan Cao 006; Quanquan Gu; Tong Zhang 001 (CoRR, 2020)

Exploring Private Federated Learning with Laplacian Smoothing. - Zhicong Liang; Bao Wang 001; Quanquan Gu; Stanley J. Osher; Yuan Yao (CoRR, 2020)

Revisiting Membership Inference Under Realistic Assumptions. - Bargav Jayaraman; Lingxiao Wang 001; David Evans 001; Quanquan Gu (CoRR, 2020)

Minimax Optimal Reinforcement Learning for Discounted MDPs. - Jiafan He; Dongruo Zhou; Quanquan Gu (CoRR, 2020)

Does Network Width Really Help Adversarial Robustness? - Boxi Wu; Jinghui Chen; Deng Cai 001; Xiaofei He 001; Quanquan Gu (CoRR, 2020)

Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu (CoRR, 2020)

Provable Multi-Objective Reinforcement Learning with Generative Models. - Dongruo Zhou; Jiahao Chen; Quanquan Gu (CoRR, 2020)

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

Gradient descent optimizes over-parameterized deep ReLU networks. - Difan Zou; Yuan Cao 006; Dongruo Zhou; Quanquan Gu (Mach. Learn., 2020)

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

Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. - Dongruo Zhou; Quanquan Gu; Csaba Szepesvári (COLT, 2021)

How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? - Zixiang Chen; Yuan Cao 006; Difan Zou; Quanquan Gu (ICLR, 2021)

Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu (ICLR, 2021)

Neural Thompson Sampling. - Weitong Zhang; Dongruo Zhou; Lihong Li 001; Quanquan Gu (ICLR, 2021)

Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. - Spencer Frei; Yuan Cao 006; Quanquan Gu (ICML, 2021)

Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. - Spencer Frei; Yuan Cao 006; Quanquan Gu (ICML, 2021)

Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. - Jiafan He; Dongruo Zhou; Quanquan Gu (ICML, 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)

Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. - Dongruo Zhou; Jiafan He; Quanquan Gu (ICML, 2021)

Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. - Difan Zou; Spencer Frei; Quanquan Gu (ICML, 2021)

On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. - Difan Zou; Quanquan Gu (ICML, 2021)

Towards Understanding the Spectral Bias of Deep Learning. - Yuan Cao 006; Zhiying Fang; Yue Wu; Ding-Xuan Zhou; Quanquan Gu (IJCAI, 2021)

Variance-reduced First-order Meta-learning for Natural Language Processing Tasks. - Lingxiao Wang 001; Kevin Huang 002; Tengyu Ma 001; Quanquan Gu; Jing Huang 019 (NAACL-HLT, 2021)

Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. - Yuan Cao 006; Quanquan Gu; Mikhail Belkin (NeurIPS, 2021)

Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent. - Spencer Frei; Quanquan Gu (NeurIPS, 2021)

Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. - Jiafan He; Dongruo Zhou; Quanquan Gu (NeurIPS, 2021)

Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. - Jiafan He; Dongruo Zhou; Quanquan Gu (NeurIPS, 2021)

Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. - Hanxun Huang; Yisen Wang 001; Sarah M. Erfani; Quanquan Gu; James Bailey 001; Xingjun Ma (NeurIPS, 2021)

Variance-Aware Off-Policy Evaluation with Linear Function Approximation. - Yifei Min; Tianhao Wang 002; Dongruo Zhou; Quanquan Gu (NeurIPS, 2021)

Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints. - Tianhao Wang 002; Dongruo Zhou; Quanquan Gu (NeurIPS, 2021)

Do Wider Neural Networks Really Help Adversarial Robustness? - Boxi Wu; Jinghui Chen; Deng Cai 001; Xiaofei He 001; Quanquan Gu (NeurIPS, 2021)

Iterative Teacher-Aware Learning. - Luyao Yuan; Dongruo Zhou; Junhong Shen; Jingdong Gao; Jeffrey L. Chen; Quanquan Gu; Ying Nian Wu; Song-Chun Zhu (NeurIPS, 2021)

Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. - Weitong Zhang; Dongruo Zhou; Quanquan Gu (NeurIPS, 2021)

Pure Exploration in Kernel and Neural Bandits. - Yinglun Zhu; Dongruo Zhou; Ruoxi Jiang; Quanquan Gu; Rebecca Willett; Robert Nowak 001 (NeurIPS, 2021)

The Benefits of Implicit Regularization from SGD in Least Squares Problems. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Dean P. Foster; Sham M. Kakade (NeurIPS, 2021)

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

Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation. - Zixiang Chen; Dongruo Zhou; Quanquan Gu (CoRR, 2021)

Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation. - Jiafan He; Dongruo Zhou; Quanquan Gu (CoRR, 2021)

Batched Neural Bandits. - Quanquan Gu; Amin Karbasi; Khashayar Khosravi; Vahab S. Mirrokni; Dongruo Zhou (CoRR, 2021)

Provably Efficient Representation Learning in Low-rank Markov Decision Processes. - Weitong Zhang; Jiafan He; Dongruo Zhou; Amy Zhang 001; Quanquan Gu (CoRR, 2021)

Adaptive Differentially Private Empirical Risk Minimization. - Xiaoxia Wu; Lingxiao Wang 001; Irina Cristali; Quanquan Gu; Rebecca Willett (CoRR, 2021)

Linear Contextual Bandits with Adversarial Corruptions. - Heyang Zhao; Dongruo Zhou; Quanquan Gu (CoRR, 2021)

Revisiting Membership Inference Under Realistic Assumptions. - Bargav Jayaraman; Lingxiao Wang 001; Katherine Knipmeyer; Quanquan Gu; David Evans 001 (Proc. Priv. Enhancing Technol., 2021)

Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. - Bao Wang 001; Difan Zou; Quanquan Gu; Stanley J. Osher (SIAM J. Sci. Comput., 2021)

Efficient Robust Training via Backward Smoothing. - Jinghui Chen; Yu Cheng 001; Zhe Gan; Quanquan Gu; Jingjing Liu 001 (AAAI, 2022)

Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes. - Chonghua Liao; Jiafan He; Quanquan Gu (ACML, 2022)

Self-training Converts Weak Learners to Strong Learners in Mixture Models. - Spencer Frei; Difan Zou; Zixiang Chen; Quanquan Gu (AISTATS, 2022)

Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. - Jiafan He; Dongruo Zhou; Quanquan Gu (AISTATS, 2022)

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)

Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. - Yue Wu; Dongruo Zhou; Quanquan Gu (AISTATS, 2022)

Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. - Zixiang Chen; Dongruo Zhou; Quanquan Gu (ALT, 2022)

Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. - Zixiang Chen; Dongruo Zhou; Quanquan Gu (ALT, 2022)

Machine-Learning-based Predictive Control of Nonlinear Processes with Uncertainty. - Zhe Wu 004; Aisha Alnajdi; Quanquan Gu; Panagiotis D. Christofides (ACC, 2022)

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

Learning Neural Contextual Bandits through Perturbed Rewards. - Yiling Jia; Weitong Zhang; Dongruo Zhou; Quanquan Gu; Hongning Wang (ICLR, 2022)

On the Convergence of Certified Robust Training with Interval Bound Propagation. - Yihan Wang; Zhouxing Shi; Quanquan Gu; Cho-Jui Hsieh (ICLR, 2022)

On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs. - Yuanzhou Chen; Jiafan He; Quanquan Gu (ICML, 2022)

Learning Stochastic Shortest Path with Linear Function Approximation. - Yifei Min; Jiafan He; Tianhao Wang 002; Quanquan Gu (ICML, 2022)

Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (ICML, 2022)

Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. - Dongruo Zhou; Quanquan Gu (ICML, 2022)

Benign Overfitting in Two-layer Convolutional Neural Networks. - Yuan Cao 006; Zixiang Chen; Misha Belkin; Quanquan Gu (NeurIPS, 2022)

Towards Understanding the Mixture-of-Experts Layer in Deep Learning. - Zixiang Chen; Yihe Deng; Yue Wu; Quanquan Gu; Yuanzhi Li (NeurIPS, 2022)

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. - Jiafan He; Tianhao Wang 002; Yifei Min; Quanquan Gu (NeurIPS, 2022)

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. - Jiafan He; Dongruo Zhou; Tong Zhang 001; Quanquan Gu (NeurIPS, 2022)

Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. - Chris Junchi Li; Dongruo Zhou; Quanquan Gu; Michael I. Jordan (NeurIPS, 2022)

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

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. - Jingfeng Wu; Difan Zou; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (NeurIPS, 2022)

Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. - Dongruo Zhou; Quanquan Gu (NeurIPS, 2022)

Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (NeurIPS, 2022)

Learning Contextual Bandits Through Perturbed Rewards. - Yiling Jia; Weitong Zhang; Dongruo Zhou; Quanquan Gu; Hongning Wang (CoRR, 2022)

Benign Overfitting in Two-layer Convolutional Neural Networks. - Yuan Cao 006; Zixiang Chen; Mikhail Belkin; Quanquan Gu (CoRR, 2022)

Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds. - Heyang Zhao; Dongruo Zhou; Jiafan He; Quanquan Gu (CoRR, 2022)

Towards Understanding Mixture of Experts in Deep Learning. - Zixiang Chen; Yihe Deng; Yue Wu; Quanquan Gu; Yuanzhi Li (CoRR, 2022)

Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. - Chris Junchi Li; Dongruo Zhou; Quanquan Gu; Michael I. Jordan (CoRR, 2022)

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. - Zixiang Chen; Chris Junchi Li; Angela Yuan; Quanquan Gu; Michael I. Jordan (CoRR, 2022)

The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. - Yuan Cao 006; Difan Zou; Yuanzhi Li; Quanquan Gu (COLT, 2023)

Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. - Heyang Zhao; Jiafan He; Dongruo Zhou; Tong Zhang 001; Quanquan Gu (COLT, 2023)

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. - Zixiang Chen; Chris Junchi Li; Huizhuo Yuan; Quanquan Gu; Michael I. Jordan (ICLR, 2023)

How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. - Yiwen Kou; Zixiang Chen; Yuan Cao 006; Quanquan Gu (ICLR, 2023)

Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. - Difan Zou; Yuan Cao 006; Yuanzhi Li; Quanquan Gu (ICLR, 2023)

Understanding Train-Validation Split in Meta-Learning with Neural Networks. - Xinzhe Zuo; Zixiang Chen; Huaxiu Yao; Yuan Cao 006; Quanquan Gu (ICLR, 2023)

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. - Qiwei Di; Jiafan He; Dongruo Zhou; Quanquan Gu (ICML, 2023)

DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. - Jiaqi Guan; Xiangxin Zhou; Yuwei Yang; Yu Bao; Jian Peng 001; Jianzhu Ma; Qiang Liu 006; Liang Wang 001; Quanquan Gu (ICML, 2023)

Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. - Jiafan He; Heyang Zhao; Dongruo Zhou; Quanquan Gu (ICML, 2023)

Benign Overfitting in Two-layer ReLU Convolutional Neural Networks. - Yiwen Kou; Zixiang Chen; Yuanzhou Chen; Quanquan Gu (ICML, 2023)

Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. - Chris Junchi Li; Huizhuo Yuan; Gauthier Gidel; Quanquan Gu; Michael I. Jordan (ICML, 2023)

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. - Yifei Min; Jiafan He; Tianhao Wang 002; Quanquan Gu (ICML, 2023)

Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (ICML, 2023)

Personalized Federated Learning under Mixture of Distributions. - Yue Wu; Shuaicheng Zhang; Wenchao Yu; Yanchi Liu; Quanquan Gu; Dawei Zhou 003; Haifeng Chen; Wei Cheng 002 (ICML, 2023)

Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. - Chenlu Ye; Wei Xiong 015; Quanquan Gu; Tong Zhang 001 (ICML, 2023)

On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. - Weitong Zhang; Jiafan He; Zhiyuan Fan; Quanquan Gu (ICML, 2023)

Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs. - Junkai Zhang; Weitong Zhang; Quanquan Gu (ICML, 2023)

Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. - Heyang Zhao; Dongruo Zhou; Jiafan He; Quanquan Gu (ICML, 2023)

Structure-informed Language Models Are Protein Designers. - Zaixiang Zheng; Yifan Deng; Dongyu Xue; Yi Zhou 018; Fei Ye; Quanquan Gu (ICML, 2023)

The Benefits of Mixup for Feature Learning. - Difan Zou; Yuan Cao 006; Yuanzhi Li; Quanquan Gu (ICML, 2023)

Why Does Sharpness-Aware Minimization Generalize Better Than SGD? - Zixiang Chen; Junkai Zhang; Yiwen Kou; Xiangning Chen; Cho-Jui Hsieh; Quanquan Gu (NeurIPS, 2023)

Robust Learning with Progressive Data Expansion Against Spurious Correlation. - Yihe Deng; Yu Yang 007; Baharan Mirzasoleiman; Quanquan Gu (NeurIPS, 2023)

Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data. - Yiwen Kou; Zixiang Chen; Quanquan Gu (NeurIPS, 2023)

Corruption-Robust Offline Reinforcement Learning with General Function Approximation. - Chenlu Ye; Rui Yang 010; Quanquan Gu; Tong Zhang 001 (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)

Efficient Privacy-Preserving Stochastic Nonconvex Optimization. - Lingxiao Wang 001; Bargav Jayaraman; David Evans 001; Quanquan Gu (UAI, 2023)

Benign Overfitting in Adversarially Robust Linear Classification. - Jinghui Chen; Yuan Cao 006; Quanquan Gu (UAI, 2023)

Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. - Yue Wu; Jiafan He; Quanquan Gu (UAI, 2023)

Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. - Weitong Zhang; Jiafan He; Dongruo Zhou; Amy Zhang 001; Quanquan Gu (UAI, 2023)

Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (CoRR, 2023)

Benign Overfitting for Two-layer ReLU Networks. - Yiwen Kou; Zixiang Chen; Yuanzhou Chen; Quanquan Gu (CoRR, 2023)

Borda Regret Minimization for Generalized Linear Dueling Bandits. - Yue Wu; Tao Jin 002; Hao Lou; Farzad Farnoud; Quanquan Gu (CoRR, 2023)

Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models. - Chen Ling 003; Xujiang Zhao; Jiaying Lu 001; Chengyuan Deng; Can Zheng; Junxiang Wang; Tanmoy Chowdhury; Yun Li 005; Hejie Cui; Xuchao Zhang; Tianjiao Zhao; Amit Panalkar; et al. (CoRR, 2023)

Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning. - Jiasheng Ye; Zaixiang Zheng; Yu Bao; Lihua Qian; Quanquan Gu (CoRR, 2023)

Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves. - Yihe Deng; Weitong Zhang; Zixiang Chen; Quanquan Gu (CoRR, 2023)

Fast Sampling via De-randomization for Discrete Diffusion Models. - Zixiang Chen; Huizhuo Yuan; Yongqian Li; Yiwen Kou; Junkai Zhang; Quanquan Gu (CoRR, 2023)

Benign Overfitting of Constant-Stepsize SGD for Linear Regression. - Difan Zou; Jingfeng Wu; Vladimir Braverman; Quanquan Gu; Sham M. Kakade (J. Mach. Learn. Res., 2023)

Large Language Models Can Be Contextual Privacy Protection Learners. - Yijia Xiao; Yiqiao Jin; Yushi Bai; Yue Wu; Xianjun Yang; Xiao Luo 001; Wenchao Yu; Xujiang Zhao; Yanchi Liu; Quanquan Gu; Haifeng Chen; Wei Wang 010; et al. (EMNLP, 2024)

Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP. - Zixiang Chen; Yihe Deng; Yuanzhi Li; Quanquan Gu (ICLR, 2024)

Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits. - Qiwei Di; Tao Jin 002; Yue Wu; Heyang Zhao; Farzad Farnoud; Quanquan Gu (ICLR, 2024)

Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning. - Qiwei Di; Heyang Zhao; Jiafan He; Quanquan Gu (ICLR, 2024)

Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. - Kaixuan Ji; Qingyue Zhao; Jiafan He; Weitong Zhang; Quanquan Gu (ICLR, 2024)

Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. - Xuheng Li; Yihe Deng; Jingfeng Wu; Dongruo Zhou; Quanquan Gu (ICLR, 2024)

How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? - Jingfeng Wu; Difan Zou; Zixiang Chen; Vladimir Braverman; Quanquan Gu; Peter L. Bartlett (ICLR, 2024)

DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization. - Xiangxin Zhou; Xiwei Cheng; Yuwei Yang; Yu Bao; Liang Wang 001; Quanquan Gu (ICLR, 2024)

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models. - Zixiang Chen; Yihe Deng; Huizhuo Yuan; Kaixuan Ji; Quanquan Gu (ICML, 2024)

Position: TrustLLM: Trustworthiness in Large Language Models. - Yue Huang; Lichao Sun 001; Haoran Wang 005; Siyuan Wu 001; Qihui Zhang; Yuan Li; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang; Xiner Li; Hanchi Sun; et al. (ICML, 2024)

Feel-Good Thompson Sampling for Contextual Dueling Bandits. - Xuheng Li; Heyang Zhao; Quanquan Gu (ICML, 2024)

Protein Conformation Generation via Force-Guided SE(3) Diffusion Models. - Yan Wang; Lihao Wang; Yuning Shen; Yiqun Wang; Huizhuo Yuan; Yue Wu; Quanquan Gu (ICML, 2024)

Diffusion Language Models Are Versatile Protein Learners. - Xinyou Wang; Zaixiang Zheng; Fei Ye; Dongyu Xue; Shujian Huang; Quanquan Gu (ICML, 2024)

Borda Regret Minimization for Generalized Linear Dueling Bandits. - Yue Wu; Tao Jin 002; Qiwei Di; Hao Lou; Farzad Farnoud; Quanquan Gu (ICML, 2024)

Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. - Chenlu Ye; Jiafan He; Quanquan Gu; Tong Zhang 001 (ICML, 2024)

Uncertainty-Aware Reward-Free Exploration with General Function Approximation. - Junkai Zhang; Weitong Zhang; Dongruo Zhou; Quanquan Gu (ICML, 2024)

Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time. - Zixiang Chen; Huizhuo Yuan; Yongqian Li; Yiwen Kou; Junkai Zhang; Quanquan Gu (NeurIPS, 2024)

Enhancing Large Vision Language Models with Self-Training on Image Comprehension. - Yihe Deng; Pan Lu; Fan Yin; Ziniu Hu; Sheng Shen 001; Quanquan Gu; James Y. Zou; Kai-Wei Chang; Wei Wang 010 (NeurIPS, 2024)

Matching the Statistical Query Lower Bound for k-Sparse Parity Problems with Sign Stochastic Gradient Descent. - Yiwen Kou; Zixiang Chen; Quanquan Gu; Sham M. Kakade (NeurIPS, 2024)

Self-Play Fine-tuning of Diffusion Models for Text-to-image Generation. - Huizhuo Yuan; Zixiang Chen; Kaixuan Ji; Quanquan Gu (NeurIPS, 2024)

Achieving Constant Regret in Linear Markov Decision Processes. - Weitong Zhang; Zhiyuan Fan; Jiafan He; Quanquan Gu (NeurIPS, 2024)

A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation. - Heyang Zhao; Jiafan He; Quanquan Gu (NeurIPS, 2024)

Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization. - Xiangxin Zhou; Dongyu Xue; Ruizhe Chen; Zaixiang Zheng; Liang Wang 001; Quanquan Gu (NeurIPS, 2024)

Pure Exploration in Asynchronous Federated Bandits. - Zichen Wang; Chuanhao Li; Chenyu Song; Lianghui Wang; Quanquan Gu; Huazheng Wang (UAI, 2024)

Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems. - Zijie Huang 002; Jeehyun Hwang; Junkai Zhang; Jinwoo Baik; Weitong Zhang; Dominik Wodarz; Yizhou Sun; Quanquan Gu; Wei Wang 010 (WWW, 2024)

TrustLLM: Trustworthiness in Large Language Models. - Lichao Sun 001; Yue Huang 001; Haoran Wang 005; Siyuan Wu 001; Qihui Zhang; Chujie Gao; Yixin Huang; Wenhan Lyu; Yixuan Zhang 001; Xiner Li; Zhengliang Liu; Yixin Liu 002; et al. (CoRR, 2024)

Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance. - Linxi Zhao; Yihe Deng; Weitong Zhang; Quanquan Gu (CoRR, 2024)

Reinforcement Learning from Human Feedback with Active Queries. - Kaixuan Ji; Jiafan He; Quanquan Gu (CoRR, 2024)

Settling Constant Regrets in Linear Markov Decision Processes. - Weitong Zhang; Zhiyuan Fan; Jiafan He; Quanquan Gu (CoRR, 2024)

Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback. - Qiwei Di; Jiafan He; Quanquan Gu (CoRR, 2024)

Matching the Statistical Query Lower Bound for k-sparse Parity Problems with Stochastic Gradient Descent. - Yiwen Kou; Zixiang Chen; Quanquan Gu; Sham M. Kakade (CoRR, 2024)

Decomposed Direct Preference Optimization for Structure-Based Drug Design. - Xiwei Cheng; Xiangxin Zhou; Yuwei Yang; Yu Bao; Quanquan Gu (CoRR, 2024)

Relative-Translation Invariant Wasserstein Distance. - Binshuai Wang; Qiwei Di; Ming Yin 003; Mengdi Wang; Quanquan Gu; Peng Wei (CoRR, 2024)

General Preference Modeling with Preference Representations for Aligning Language Models. - Yifan Zhang; Ge Zhang; Yue Wu; Kangping Xu; Quanquan Gu (CoRR, 2024)

Accelerated Preference Optimization for Large Language Model Alignment. - Jiafan He; Huizhuo Yuan; Quanquan Gu (CoRR, 2024)

Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization. - Guanlin Liu; Kaixuan Ji; Renjie Zheng; Zheng Wu; Chen Dun; Quanquan Gu; Lin Yan (CoRR, 2024)

CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing. - Chen Yang; Chenyang Zhao; Quanquan Gu; Dongruo Zhou (CoRR, 2024)

Sharp Analysis for KL-Regularized Contextual Bandits and RLHF. - Heyang Zhao; Chenlu Ye; Quanquan Gu; Tong Zhang 001 (CoRR, 2024)

MARS: Unleashing the Power of Variance Reduction for Training Large Models. - Huizhuo Yuan; Yifeng Liu 004; Shuang Wu; Xun Zhou; Quanquan Gu (CoRR, 2024)

ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design. - Yiming Ma; Fei Ye; Yi Zhou 018; Zaixiang Zheng; Dongyu Xue; Quanquan Gu (CoRR, 2024)

Towards Simple and Provable Parameter-Free Adaptive Gradient Methods. - Yuanzhe Tao; Huizhuo Yuan; Xun Zhou; Yuan Cao 006; Quanquan Gu (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)

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. - Dongruo Zhou; Jinghui Chen; Yuan Cao 006; Ziyan Yang; Quanquan Gu (Trans. Mach. Learn. Res., 2024)

On the Power of Multitask Representation Learning with Gradient Descent. - Qiaobo Li; Zixiang Chen; Yihe Deng; Yiwen Kou; Yuan Cao 006; Quanquan Gu (AISTATS, 2025)

LLaVA-Critic: Learning to Evaluate Multimodal Models. - Tianyi Xiong; Xiyao Wang; Dong Guo; Qinghao Ye; Haoqi Fan 001; Quanquan Gu; Heng Huang; Chunyuan Li (CVPR, 2025)

Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers. - Runjia Li; Qiwei Di; Quanquan Gu (ICLR, 2025)

DPLM-2: A Multimodal Diffusion Protein Language Model. - Xinyou Wang; Zaixiang Zheng; Fei Ye; Dongyu Xue; Shujian Huang; Quanquan Gu (ICLR, 2025)

Self-Play Preference Optimization for Language Model Alignment. - Yue Wu; Zhiqing Sun; Huizhuo Yuan; Kaixuan Ji; Yiming Yang; Quanquan Gu (ICLR, 2025)

ProteinBench: A Holistic Evaluation of Protein Foundation Models. - Fei Ye; Zaixiang Zheng; Dongyu Xue; Yuning Shen; Lihao Wang; Yiming Ma; Yan Wang; Xinyou Wang; Xiangxin Zhou; Quanquan Gu (ICLR, 2025)

Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis. - Zikun Zhang; Zixiang Chen; Quanquan Gu (ICLR, 2025)

Energy-Weighted Flow Matching for Offline Reinforcement Learning. - Shiyuan Zhang; Weitong Zhang; Quanquan Gu (ICLR, 2025)

Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration. - Heyang Zhao; Xingrui Yu; David Mark Bossens; Ivor W. Tsang; Quanquan Gu (ICLR, 2025)

CryoFM: A Flow-based Foundation Model for Cryo-EM Densities. - Yi Zhou; Yilai Li; Jing Yuan; Quanquan Gu (ICLR, 2025)

Guided Discrete Diffusion for Electronic Health Record Generation. - Jun Han; Zixiang Chen; Yongqian Li; Yiwen Kou; Eran Halperin; Robert E. Tillman; Quanquan Gu (Trans. Mach. Learn. Res., 2025)

Tensor Product Attention Is All You Need. - Yifan Zhang; Yifeng Liu 004; Huizhuo Yuan; Zhen Qin; Yang Yuan; Quanquan Gu; Andrew Chi-Chih Yao (CoRR, 2025)

Nearly Optimal Sample Complexity of Offline KL-Regularized Contextual Bandits under Single-Policy Concentrability. - Qingyue Zhao; Kaixuan Ji; Heyang Zhao; Tong Zhang 001; Quanquan Gu (CoRR, 2025)

Logarithmic Regret for Online KL-Regularized Reinforcement Learning. - Heyang Zhao; Chenlu Ye; Wei Xiong 015; Quanquan Gu; Tong Zhang 001 (CoRR, 2025)

Multi-Step Alignment as Markov Games: An Optimistic Online Gradient Descent Approach with Convergence Guarantees. - Yongtao Wu; Luca Viano; Yihang Chen; Zhenyu Zhu; Kimon Antonakopoulos; Quanquan Gu; Volkan Cevher (CoRR, 2025)

Understanding SGD with Exponential Moving Average: A Case Study in Linear Regression. - Xuheng Li; Quanquan Gu (CoRR, 2025)

Game-Theoretic Regularized Self-Play Alignment of Large Language Models. - Xiaohang Tang; Sangwoong Yoon; Seongho Son; Huizhuo Yuan; Quanquan Gu; Ilija Bogunovic (CoRR, 2025)

Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μP Parametrization. - Zixiang Chen; Greg Yang; Qingyue Zhao; Quanquan Gu (CoRR, 2025)

Variance-Dependent Regret Lower Bounds for Contextual Bandits. - Jiafan He; Quanquan Gu (CoRR, 2025)

Elucidating the Design Space of Multimodal Protein Language Models. - Cheng-Yen Hsieh; Xinyou Wang; Daiheng Zhang; Dongyu Xue; Fei Ye; Shujian Huang; Zaixiang Zheng; Quanquan Gu (CoRR, 2025)

An All-Atom Generative Model for Designing Protein Complexes. - Ruizhe Chen; Dongyu Xue; Xiangxin Zhou; Zaixiang Zheng; Xiangxiang Zeng; Quanquan Gu (CoRR, 2025)

Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression. - Yuning Shen; Lihao Wang; Huizhuo Yuan; Yan Wang; Bangji Yang; Quanquan Gu (CoRR, 2025)

On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning. - Yifan Zhang; Yifeng Liu 004; Huizhuo Yuan; Yang Yuan; Quanquan Gu; Andrew C. Yao (CoRR, 2025)

Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling. - Xiangxin Zhou; Mingyu Li; Yi Xiao; Jiahan Li; Dongyu Xue; Zaixiang Zheng; Jianzhu Ma; Quanquan Gu (CoRR, 2025)

SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving. - Wendong Xu; Jing Xiong; Chenyang Zhao; Qiujiang Chen; Haoran Wang; Hui Shen; Zhongwei Wan; Jianbo Dai; Taiqiang Wu; He Xiao; Chaofan Tao; Z. Morley Mao; et al. (CoRR, 2025)