Quanquan Gu

发表

Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements

Xiao Zhang, Lingxiao Wang, Quanquan Gu, 2017, 1701.00481.

A Nonconvex Free Lunch for Low-Rank plus Sparse Matrix Recovery

Xiao Zhang, Lingxiao Wang, Quanquan Gu, 2017 .

A Unified Framework for Low-Rank plus Sparse Matrix Recovery

Xiao Zhang, Lingxiao Wang, Quanquan Gu, 2017, 1702.06525.

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks pdf

Jinghui Chen, Quanquan Gu, Quanquan Gu, 2018, IJCAI.

Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs

Chris H. Q. Ding, Quanquan Gu, Jie Zhou, 2010, SDM.

Collaborative Filtering with Entity Similarity Regularization in Heterogeneous Information Networks

Quanquan Gu, Yizhou Sun, Jiawei Han, 2013 .

Co-clustering on manifolds

Quanquan Gu, Jie Zhou, Quanquan Gu, 2009, KDD.

Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks

Yuan Cao, Quanquan Gu, Difan Zou, 2018, ArXiv.

Local Learning Regularized Nonnegative Matrix Factorization

Quanquan Gu, Jie Zhou, Quanquan Gu, 2009, IJCAI.

Local and Global Inference for High Dimensional Nonparanormal Graphical Models

Han Liu, Quanquan Gu, Y. Ning, 2015, 1502.02347.

Local and Global Inference for High Dimensional Gaussian Copula Graphical Models

Han Liu, Quanquan Gu, Yuan Cao, 2015 .

High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality

Zhaoran Wang, Quanquan Gu, Han Liu, 2015, NIPS.

Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks

Quanquan Gu, Yuan Cao, Quanquan Gu, 2019, AAAI.

Stochastic Nested Variance Reduction for Nonconvex Optimization

Quanquan Gu, Pan Xu, Dongruo Zhou, 2018, J. Mach. Learn. Res..

Aggregating Private Sparse Learning Models Using Multi-Party Computation

Quanquan Gu, Lu Tian, Bargav Jayaraman, 2016 .

On Trivial Solution and Scale Transfer Problems in Graph Regularized NMF

Chris H. Q. Ding, Jiawei Han, Quanquan Gu, 2011, IJCAI.

Identifying gene regulatory network rewiring using latent differential graphical models

Quanquan Gu, Dechao Tian, Jian Ma, 2016, Nucleic acids research.

Finding Local Minima via Stochastic Nested Variance Reduction pdf

Quanquan Gu, Pan Xu, Dongruo Zhou, 2018, ArXiv.

On the Global Convergence of Training Deep Linear ResNets pdf

Philip M. Long, Quanquan Gu, Difan Zou, 2020, ICLR.

Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs

Quanquan Gu, Jiafan He, Dongruo Zhou, 2021, AISTATS.

A Finite Time Analysis of Two Time-Scale Actor Critic Methods pdf

Quanquan Gu, Pan Xu, Yue Wu, 2020, NeurIPS.

High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality

Zhaoran Wang, Han Liu, Quanquan Gu, 2014, 1412.8729.

A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks

Quanquan Gu, Tong Zhang, Zixiang Chen, 2020, NeurIPS.

Efficient Robust Training via Backward Smoothing

Quanquan Gu, Yu Cheng, J. Chen, 2020, AAAI.

Do Wider Neural Networks Really Help Adversarial Robustness?

Quanquan Gu, Xiaofei He, Deng Cai, 2020, NeurIPS.

High Dimensional Multivariate Regression and Precision Matrix Estimation via Nonconvex Optimization

Quanquan Gu, J. Chen, Jinghui Chen, 2016, 1606.00832.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support

Joshua C. Chang, Thomas J. Hladish, Carl A. B. Pearson, 2020, medRxiv.

Communication-e � cient Distributed Sparse Linear Discriminant Analysis

Quanquan Gu, Lu Tian, 2017 .

Communication-efficient Distributed Estimation and Inference for Transelliptical Graphical Models

Quanquan Gu, Pan Xu, Lu Tian, 2016, 1612.09297.

Neighborhood Preserving Nonnegative Matrix

Quanquan Gu, Jie Zhou, 2009 .

Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs

Quanquan Gu, Jiafan He, Dongruo Zhou, 2020, NeurIPS.

Sampling from Non-Log-Concave Distributions via Stochastic Variance-Reduced Gradient Langevin Dynamics

Quanquan Gu, Difan Zou, Pan Xu, 2019 .

Unsupervised Link Selection in Networks

Charu C. Aggarwal, Jiawei Han, Quanquan Gu, 2013, AISTATS.

Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games

Quanquan Gu, Dongruo Zhou, Zixiang Chen, 2021, ALT.

NCIS: A NETWORK-ASSISTED CO-CLUSTERING ALGORITHM TO DISCOVER CANCER SUBTYPES BASED ON GENE EXPRESSION BY

Jack P. Hou, Quanquan Gu, J. P. Hou, 2013 .

On the statistical limits of convex relaxations: a case study

Han Liu, Quanquan Gu, Zhaoran Wang, 2016, ICML 2016.

DP-LSSGD: An Optimization Method to Lift the Utility in Privacy-Preserving ERM

Quanquan Gu, Farzin Barekat, Stanley Osher, 2019 .

NeuralUCB: Contextual Bandits with Neural Network-Based Exploration

Quanquan Gu, Lihong Li, Dongruo Zhou, 2019 .

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks

Jinghui Chen, Yuan Cao, Quanquan Gu, 2020, IJCAI.

Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins

Quanquan Gu, Spencer Frei, Yuan Cao, 2020, ICML.

How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? pdf

Yuan Cao, Quanquan Gu, Zixiang Chen, 2019, ICLR.

Active Learning: A Survey

Philip S. Yu, Charu C. Aggarwal, Jiawei Han, 2014, Data Classification: Algorithms and Applications.

On the Dynamics and Convergence of Weight Normalization for Training Neural Networks

Quanquan Gu, Guido Montúfar, Yonatan Dukler, 2019 .

Linear Contextual Bandits with Adversarial Corruptions pdf

Quanquan Gu, Dongruo Zhou, Heyang Zhao, 2021, ArXiv.

Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise pdf

Yuan Cao, Quanquan Gu, Spencer Frei, 2021, ICML.

Training Deep Neural Networks with Partially Adaptive Momentum

Jinghui Chen, Yuan Cao, Quanquan Gu, 2019 .

Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent pdf

Quanquan Gu, Spencer Frei, Quanquan Gu, 2021, NeurIPS.

Online and active learning of big networks: theory and algorithms

Quanquan Gu, Quanquan Gu, 2014 .

Iterative Teacher-Aware Learning pdf

Song-Chun Zhu, Ying Nian Wu, Quanquan Gu, 2021, NeurIPS.

On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients

Quanquan Gu, Difan Zou, Quanquan Gu, 2021, ICML.

Almost Optimal Algorithms for Two-player Zero-Sum Markov Games with Linear Function Approximation

Quanquan Gu, Dongruo Zhou, Zixiang Chen, 2021 .

Multi-task cox proportional hazard model for predicting risk of unplanned hospital readmission

Quanquan Gu, Lu Tian, Megan Grzyb, 2017, Systems and Information Engineering Design Symposium.

Fairness-Preserving Empirical Risk Minimization

V. Roychowdhury, Quanquan Gu, Junghoo Cho, 2019 .

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions

Quanquan Gu, Jiafan He, Dongruo Zhou, 2022, ArXiv.

Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions

Quanquan Gu, Jiafan He, Dongruo Zhou, 2022, NeurIPS.

The Benefits of Implicit Regularization from SGD in Least Squares Problems

Dean P. Foster, S. Kakade, Dean Phillips Foster, 2021, NeurIPS.

A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits

Quanquan Gu, Jiafan He, Yifei Min, 2022, NeurIPS.

The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift

S. Kakade, Quanquan Gu, V. Braverman, 2022, NeurIPS.

Benign Overfitting in Two-layer Convolutional Neural Networks

Mikhail Belkin, Quanquan Gu, Yuan Cao, 2022, NeurIPS.

Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium

Michael I. Jordan, Quanquan Gu, C. J. Li, 2022, ArXiv.

Learning Stochastic Shortest Path with Linear Function Approximation

Quanquan Gu, Jiafan He, Yifei Min, 2021, ICML.

A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning

Michael I. Jordan, M.I. Jordan, Quanquan Gu, 2022, ICLR.

A Finite Time Analysis of Two Time-Scale Actor Critic Methods

Quanquan Gu, Pan Xu, Yue Wu, 2020, NeurIPS.

Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent

Quanquan Gu, Spencer Frei, 2021, NeurIPS.

Stochastic Variance-Reduced Cubic Regularized Newton Method

Quanquan Gu, Dongruo Zhou, Pan Xu, 2018, ICML.

Towards Understanding Mixture of Experts in Deep Learning

Quanquan Gu, Yue Wu, Zixiang Chen, 2022, ArXiv.

Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs

Quanquan Gu, Dongruo Zhou, 2022, NeurIPS.

Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression

S. Kakade, Quanquan Gu, V. Braverman, 2021, ICML.

Faster Perturbed Stochastic Gradient Methods for Finding Local Minima

Quanquan Gu, Dongruo Zhou, Zixiang Chen, 2021, ALT.

Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes

Quanquan Gu, Jiafan He, Dongruo Zhou, 2022, ICML.

Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation

Quanquan Gu, Dongruo Zhou, Yue Wu, 2021, AISTATS.

Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes

Quanquan Gu, Wei Xiong, Chen Ye, 2022, ArXiv.

Agnostic Learning of a Single Neuron with Gradient Descent

Quanquan Gu, Yuan Cao, Spencer Frei, 2020, NeurIPS.

Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation pdf

Quanquan Gu, Dongruo Zhou, Zixiang Chen, 2021, ArXiv.

Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization

Yuan Cao, Quanquan Gu, Dongruo Zhou, 2020, AISTATS.

Lower Bounds for Smooth Nonconvex Finite-Sum Optimization pdf

Quanquan Gu, Dongruo Zhou, Quanquan Gu, 2019, ICML.

Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions

Quanquan Gu, Tianhao Wang, Pan Xu, 2018, ICML.

Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms

Quanquan Gu, Tianhao Wang, Pan Xu, 2018, AISTATS.

ClusCite: effective citation recommendation by information network-based clustering

Jiawei Han, Xiao Yu, Lidan Wang, 2014, KDD.

Local Relevance Weighted Maximum Margin Criterion for Text Classification

Quanquan Gu, Jie Zhou, Quanquan Gu, 2009, SDM.

Low-Rank and Sparse Structure Pursuit via Alternating Minimization

Zhaoran Wang, Quanquan Gu, Han Liu, 2016, AISTATS.

Robust Classification of Information Networks by Consistent Graph Learning

Jiawei Han, Quanquan Gu, Shi Zhi, 2015, ECML/PKDD.

On the Statistical Limits of Convex Relaxations

Zhaoran Wang, Quanquan Gu, Han Liu, 2015, ICML.

A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression

Jian Ma, Jiawei Han, Quanquan Gu, 2014, BMC Bioinformatics.

Transductive Classification via Dual Regularization

Quanquan Gu, Jie Zhou, Quanquan Gu, 2009, ECML/PKDD.

Continuous-trait probabilistic model for comparing multi-species functional genomic data

Quanquan Gu, R. O’Neill, D. Gilbert, 2018, bioRxiv.

Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks

Yuan Cao, Quanquan Gu, Quanquan Gu, 2019, NeurIPS.

Rank Aggregation via Heterogeneous Thurstone Preference Models

Tao Jin, Quanquan Gu, Pan Xu, 2019, AAAI.

Generalized Fisher Score for Feature Selection

Jiawei Han, Quanquan Gu, Zhenhui Li, 2011, UAI.

A new formula for the L norm

Po-Lam Yung, Qingsong Gu, Quanquan Gu, 2021 .

An Improved Analysis of Training Over-parameterized Deep Neural Networks

Quanquan Gu, Difan Zou, Quanquan Gu, 2019, NeurIPS.

Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency

Quanquan Gu, Jiafan He, Dongruo Zhou, 2023, COLT.

Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes pdf

Quanquan Gu, Dongruo Zhou, Csaba Szepesvari, 2020, COLT.

Logarithmic Regret for Reinforcement Learning with Linear Function Approximation pdf

Quanquan Gu, Dongruo Zhou, Jiafan He, 2020, ICML.

Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization

Lingxiao Wang, Quanquan Gu, David Evans, 2018, NeurIPS.

A practical algorithm for learning scene information from monocular video.

Lin Zhu, Jie Zhou, Jingyan Song, 2008, Optics express.

Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization pdf

Quanquan Gu, Difan Zou, Yuan Cao, 2021, ICLR.

Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

Yizhou Sun, Song Jiang, Quanquan Gu, 2019, NeurIPS.

Improving Neural Language Generation with Spectrum Control

Jing Huang, Lingxiao Wang, Quanquan Gu, 2020, ICLR.

Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures pdf

Mikhail Belkin, Quanquan Gu, Yuan Cao, 2021, NeurIPS.

Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise

Quanquan Gu, Yuanbin Cao, Spencer Frei, 2021, ICML.

RayS: A Ray Searching Method for Hard-label Adversarial Attack

Quanquan Gu, Jinghui Chen, 2020, Knowledge Discovery and Data Mining.

Revisiting Membership Inference Under Realistic Assumptions pdf

Lingxiao Wang, Quanquan Gu, David Evans, 2020, Proc. Priv. Enhancing Technol..

Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping pdf

Quanquan Gu, Dongruo Zhou, Jiafan He, 2020, ICML.

Learning One-hidden-layer ReLU Networks via Gradient Descent pdf

Xiao Zhang, Lingxiao Wang, Yaodong Yu, 2018, AISTATS.

Towards Understanding the Spectral Bias of Deep Learning pdf

Yuan Cao, Quanquan Gu, Ding-Xuan Zhou, 2019, IJCAI.

Statistical Machine Learning in Model Predictive Control of Nonlinear Processes

Panagiotis D. Christofides, David Rincon, Quanquan Gu, 2021, Mathematics.

Optimization Theory for ReLU Neural Networks Trained with Normalization Layers pdf

Guido Mont'ufar, Quanquan Gu, Yonatan Dukler, 2020, ICML.

Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling

Jiawei Han, Liangliang Cao, Quanquan Gu, 2012, TIST.

Towards Active Learning on Graphs: An Error Bound Minimization Approach

Jiawei Han, Quanquan Gu, Quanquan Gu, 2012, 2012 IEEE 12th International Conference on Data Mining.

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization pdf

Yuan Cao, Quanquan Gu, Dongruo Zhou, 2018, ArXiv.

Pure Exploration in Kernel and Neural Bandits

Quanquan Gu, R. Nowak, R. Willett, 2021, NeurIPS.

Semiparametric Differential Graph Models

Quanquan Gu, Pan Xu, Quanquan Gu, 2016, NIPS.

Improving Adversarial Robustness Requires Revisiting Misclassified Examples

James Bailey, Jinfeng Yi, Quanquan Gu, 2020, ICLR.

Joint Feature Selection and Subspace Learning

Jiawei Han, Quanquan Gu, Zhenhui Li, 2011, IJCAI.

A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks pdf

Yuan Cao, Quanquan Gu, Quanquan Gu, 2019, ArXiv.

Benign Overfitting for Two-layer ReLU Networks

Quanquan Gu, Zixiang Chen, Yiwen Kou, 2023, ArXiv.

Benign Overfitting of Constant-Stepsize SGD for Linear Regression pdf

Vladimir Braverman, Sham M. Kakade, Quanquan Gu, 2021, COLT.

Personalized Federated Learning under Mixture of Distributions

Quanquan Gu, Wei Cheng, Yanchi Liu, 2023, ICML.

Multi-agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

Quanquan Gu, Jiafan He, Yifei Min, 2023 .

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

Quanquan Gu, Jiafan He, Yifei Min, 2023, arXiv.org.

Variance-Aware Off-Policy Evaluation with Linear Function Approximation pdf

Quanquan Gu, Tianhao Wang, Yifei Min, 2021, NeurIPS.

An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient pdf

Quanquan Gu, Pan Xu, Felicia Gao, 2019, UAI.

Personalized entity recommendation: a heterogeneous information network approach

Yizhou Sun, Quanquan Gu, Xiang Ren, 2014, WSDM.

Neural Contextual Bandits with UCB-based Exploration

Quanquan Gu, Lihong Li, Dongruo Zhou, 2019, ICML.

Neural Contextual Bandits with Upper Confidence Bound-Based Exploration pdf

Quanquan Gu, Lihong Li, Dongruo Zhou, 2019, ArXiv.

Learning a Kernel for Multi-Task Clustering

Jiawei Han, Quanquan Gu, Zhenhui Li, 2011, AAAI.

Adaptive Differentially Private Empirical Risk Minimization pdf

Rebecca Willett, Quanquan Gu, Xiaoxia Wu, 2021, ArXiv.

On the Convergence and Robustness of Adversarial Training

James Bailey, Jinfeng Yi, Bowen Zhou, 2021, ICML.

Towards Personalized Learning in Mobile Sensing Systems

Chenglin Miao, Wenyao Xu, Lu Su, 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks pdf

Quanquan Gu, Sarah Monazam Erfani, Xingjun Ma, 2021, NeurIPS.

Provable Multi-Objective Reinforcement Learning with Generative Models pdf

Jiahao Chen, Quanquan Gu, Dongruo Zhou, 2020, ArXiv.

Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo pdf

Quanquan Gu, Difan Zou, Stanley Osher, 2019, SIAM J. Sci. Comput..

Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks

Xiang Li, Jiawei Han, Xiao Yu, 2015, CIKM.

Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium

M.I. Jordan, Quanquan Gu, C. J. Li, 2022, Neural Information Processing Systems.

Stochastic Variance-Reduced Cubic Regularization Methods

Quanquan Gu, Dongruo Zhou, Pan Xu, 2019, J. Mach. Learn. Res..

The Benefits of Mixup for Feature Learning

Quanquan Gu, Difan Zou, Yuan Cao, 2023, ICML.

Sample Efficient Policy Gradient Methods with Recursive Variance Reduction

Quanquan Gu, Pan Xu, F. Gao, 2019, ICLR.

Differentially Private Hypothesis Transfer Learning

Yang Wang, Donald E. Brown, Quanquan Gu, 2018, ECML/PKDD.

Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models

Quanquan Gu, Carl Yang, Wei Cheng, 2023, ArXiv.

Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

Jinghui Chen, Quanquan Gu, Pan Xu, 2017, NeurIPS.

Local Learning Regularized Nonnegative Matrix Factorization Quanquan Gu

Quanquan Gu, Jie Zhou, 2009 .

Local Learning Regularized Nonnegative Matrix

Quanquan Gu, Jie Zhou, 2009 .

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM

Quanquan Gu, Stanley J. Osher, Farzin Barekat, 2019, MSML.

Linear Discriminant Dimensionality Reduction

Jiawei Han, Quanquan Gu, Zhenhui Li, 2011, ECML/PKDD.

Recommendation in heterogeneous information networks with implicit user feedback

Yizhou Sun, Jiawei Han, Xiao Yu, 2013, RecSys.

Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons pdf

Farzad Farnoud, Quanquan Gu, Hao Lou, 2021, AISTATS.

Two dimensional Maximum Margin Criterion

Quanquan Gu, Jie Zhou, Quanquan Gu, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery

Xiao Zhang, Lingxiao Wang, Quanquan Gu, 2018, AISTATS.

Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization

Jinghui Chen, Quanquan Gu, Quanquan Gu, 2017, KDD.

Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization

Jian Ma, Quanquan Gu, Pan Xu, 2017, NIPS.

Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization

Jinghui Chen, Quanquan Gu, Quanquan Gu, 2016, UAI.

High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm

ChengXiang Zhai, Lingxiao Wang, Quanquan Gu, 2017, ICML.

Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction

Quanquan Gu, Difan Zou, Pan Xu, 2019, NeurIPS.

Robust Learning with Progressive Data Expansion Against Spurious Correlation

Quanquan Gu, Baharan Mirzasoleiman, Yihe Deng, 2023, ArXiv.

Anisotropic versions of the Brezis-Van Schaftingen-Yung approach at $s=1$ and $s=0$

Qingzhong Huang, Qingsong Gu, Quanquan Gu, 2022, 2201.00353.

The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks

Quanquan Gu, Difan Zou, Yuanbin Cao, 2023, ArXiv.

Is neuron coverage a meaningful measure for testing deep neural networks?

Miryung Kim, Lingxiao Wang, Quanquan Gu, 2020, ESEC/SIGSOFT FSE.

Does Network Width Really Help Adversarial Robustness? pdf

Deng Cai, Xiaofei He, Jinghui Chen, 2020, ArXiv.

Stochastic Recursive Variance-Reduced Cubic Regularization Methods pdf

Quanquan Gu, Dongruo Zhou, Quanquan Gu, 2019, AISTATS.

Towards feature selection in network

Jiawei Han, Quanquan Gu, Quanquan Gu, 2011, CIKM '11.

A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression

Jack P. Hou, Quanquan Gu, Jiawei Han, 2014, BMC Bioinformatics.

A Framework of Mining Trajectories from Untrustworthy Data in Cyber-Physical System

Thomas F. La Porta, Jiawei Han, Xiao Yu, 2015, ACM Trans. Knowl. Discov. Data.

A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation

Xiao Zhang, Lingxiao Wang, Quanquan Gu, 2016, AISTATS.

A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery

Xiao Zhang, Lingxiao Wang, Yaodong Yu, 2018, ICML.

Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference

Quanquan Gu, Pan Xu, Aditya Chaudhry, 2017, ICML.

Citation Prediction in Heterogeneous Bibliographic Networks

Jiawei Han, Xiao Yu, Quanquan Gu, 2012, SDM.

Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models

Quanquan Gu, Carl Yang, Wei Cheng, 2023, ArXiv.

Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization

Quanquan Gu, Pan Xu, Dongruo Zhou, 2018, NeurIPS.

Stochastic Variance-Reduced Hamilton Monte Carlo Methods pdf

Quanquan Gu, Pan Xu, Difan Zou, 2018, ICML.

Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing

Quanquan Gu, Rongda Zhu, Quanquan Gu, 2015, ICML.

Exploring Private Federated Learning with Laplacian Smoothing pdf

Quanquan Gu, Yuan Yao, Zhicong Liang, 2020, ArXiv.

On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs

Quanquan Gu, Jiafan He, Yuanzhou Chen, 2022, ICML.

On the Convergence of Certified Robust Training with Interval Bound Propagation

Cho-Jui Hsieh, Quanquan Gu, Zhouxing Shi, 2022, ICLR.

Learning Neural Contextual Bandits through Perturbed Rewards

Quanquan Gu, Dongruo Zhou, Weitong Zhang, 2022, ICLR.

Batched Neural Bandits pdf

Amin Karbasi, Vahab Mirrokni, Khashayar Khosravi, 2021, ArXiv.

Neural Contextual Bandits with Deep Representation and Shallow Exploration pdf

Handong Zhao, Quanquan Gu, Zheng Wen, 2020, ICLR.

Neural Thompson Sampling pdf

Quanquan Gu, Dongruo Zhou, Lihong Li, 2020, ICLR.

Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds pdf

Yuan Cao, Quanquan Gu, Zixiang Chen, 2020, ArXiv.

A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation

Quanquan Gu, Pan Xu, 2019, ICML.

O N THE C ONVERGENCE OF C ERTIFIED R OBUST T RAINING WITH I NTERVAL B OUND P ROPAGATION

Cho-Jui Hsieh, Quanquan Gu, Yihan Wang, 2022 .

Contextual Bandits in a Collaborative Environment

Quanquan Gu, Huazheng Wang, Qingyun Wu, 2016, SIGIR.

Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate

Vladimir Braverman, Quanquan Gu, Difan Zou, 2020, ICLR.

Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling pdf

Quanquan Gu, Difan Zou, Pan Xu, 2020, UAI.

Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks

Yuan Cao, Quanquan Gu, Spencer Frei, 2019, NeurIPS.

Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models pdf

Jinghui Chen, Quanquan Gu, David Evans, 2020, AISTATS.

Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension

Quanquan Gu, Jiafan He, Yue Wu, 2023, arXiv.org.

Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

Quanquan Gu, Jiafan He, Yifei Min, 2023, ICML.

Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs

Quanquan Gu, Weitong Zhang, Junkai Zhang, 2023, arXiv.org.

Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes

Quanquan Gu, Wei Xiong, Chen Ye, 2022, arXiv.org.

Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes pdf

Quanquan Gu, Jiafan He, Chonghua Liao, 2021, ACML.

Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation pdf

Quanquan Gu, Dongruo Zhou, Weitong Zhang, 2021, NeurIPS.

Provably Efficient Representation Learning in Low-rank Markov Decision Processes pdf

Quanquan Gu, Amy Zhang, Jiafan He, 2021, ArXiv.

Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation pdf

Quanquan Gu, Jiafan He, Dongruo Zhou, 2021, NeurIPS.

Locally Diﬀerentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes

Quanquan Gu, Chonghua Liao, Emtiyaz Khan, 2022 .

Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation pdf

Quanquan Gu, Dongruo Zhou, Jiafan He, 2021, ArXiv.

Communication-efficient Distributed Sparse Linear Discriminant Analysis

Lu Tian, Quanquan Gu, Quanquan Gu, 2016, AISTATS.

Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates

Lu Tian, Quanquan Gu, Pan Xu, 2016, UAI.

Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron

S. Kakade, Quanquan Gu, V. Braverman, 2023, ICML.

Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples

S. Kakade, Quanquan Gu, V. Braverman, 2023, arXiv.org.

Jiawei Han, Quanquan Gu, Zhenhui Li, 2011, CIKM '11.

Clustered Support Vector Machines

Jiawei Han, Quanquan Gu, Quanquan Gu, 2013, AISTATS.

Optimal Statistical and Computational Rates for One Bit Matrix Completion

Quanquan Gu, Renkun Ni, Quanquan Gu, 2016, AISTATS.

Online Spectral Learning on a Graph with Bandit Feedback

Jiawei Han, Quanquan Gu, Quanquan Gu, 2014, 2014 IEEE International Conference on Data Mining.

Subspace maximum margin clustering

Quanquan Gu, Jie Zhou, Quanquan Gu, 2009, CIKM.

Continuous-trait probabilistic model for comparing multi-species functional genomic data

Rachel J. O'Neill, Quanquan Gu, Yang Zhang, 2018, bioRxiv.

Minimax Optimal Reinforcement Learning for Discounted MDPs pdf

Quanquan Gu, Dongruo Zhou, Jiafan He, 2020, ArXiv.

Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics

Quanquan Gu, Pan Xu, Difan Zou, 2018, UAI.

A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks pdf

Jinfeng Yi, Jinghui Chen, Quanquan Gu, 2018, AAAI.

Neighborhood Preserving Nonnegative Matrix Factorization

Quanquan Gu, Jie Zhou, Quanquan Gu, 2009, BMVC.

Batch-Mode Active Learning via Error Bound Minimization

Jiawei Han, Tong Zhang, Quanquan Gu, 2014, UAI.

Selective sampling on graphs for classification

Charu C. Aggarwal, Jiawei Han, Jialu Liu, 2013, KDD.

Selective Labeling via Error Bound Minimization

Chris H. Q. Ding, Jiawei Han, Tong Zhang, 2012, NIPS.

A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave

G. Obozinski, D. Bertsimas, Lingxiao Wang, 2021, Nature Communications.

DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design

Quanquan Gu, Liang Wang, Jianzhu Ma, 2024, ICML.

Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization

Michael I. Jordan, Quanquan Gu, C. J. Li, 2022, ICML.

GIN: A Clustering Model for Capturing Dual Heterogeneity in Networked Data

Charu C. Aggarwal, Jiawei Han, Jing Gao, 2015, SDM.

The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks

Quanquan Gu, Difan Zou, Yuan Cao, 2023, COLT.

H OW DOES SEMI - SUPERVISED LEARNING WITH P SEUDO - LABELERS WORK ? A CASE STUDY

Quanquan Gu, Yuanbin Cao, Zixiang Chen, 2023 .

Benign Overfitting in Two-layer ReLU Convolutional Neural Networks

Quanquan Gu, Zi-Yuan Chen, Yiwen Kou, 2023, ICML.

A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery

Xiao Zhang, Lingxiao Wang, Quanquan Gu, 2017, ICML.

A Universal Variance Reduction-Based Catalyst for Nonconvex Low-Rank Matrix Recovery

Lingxiao Wang, Quanquan Gu, Xiao Zhang, 2017, 1701.02301.

Accelerated Stochastic Block Coordinate Descent with Optimal Sampling

Quanquan Gu, Aston Zhang, Quanquan Gu, 2016, KDD.

Efficient Privacy-Preserving Stochastic Nonconvex Optimization

Lingxiao Wang, Quanquan Gu, Bargav Jayaraman, 2019, UAI.

Differentially Private Federated Learning with Laplacian Smoothing

S. Osher, Quanquan Gu, Bao Wang, 2020, 2005.00218.

A Knowledge Transfer Framework for Differentially Private Sparse Learning

Lingxiao Wang, Quanquan Gu, Lingxiao Wang, 2019, AAAI.

Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning

Lingxiao Wang, Quanquan Gu, Lingxiao Wang, 2019, IJCAI.

Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits

Quanquan Gu, Jiafan He, Dongruo Zhou, 2022, ICML.

Two Dimensional Nonnegative Matrix Factorization

Quanquan Gu, Jie Zhou, Quanquan Gu, 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

Robust Learning with Progressive Data Expansion Against Spurious Correlation

Quanquan Gu, Baharan Mirzasoleiman, Yihe Deng, 2023, NeurIPS.

Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs

C. Ding, Quanquan Gu, Jie Zhou, 2010, SDM.

Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL

Quanquan Gu, Amy Zhang, Jiafan He, 2023, UAI.

A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation

Quanquan Gu, Pan Xu, 2019, ICML.

Towards Understanding the Mixture-of-Experts Layer in Deep Learning

Quanquan Gu, Yue Wu, Zixiang Chen, 2022, NeurIPS.

MOTS: Minimax Optimal Thompson Sampling

Quanquan Gu, Xiaokui Xiao, Pan Xu, 2020, ICML.

Double Explore-then-Commit: Asymptotic Optimality and Beyond pdf

Xiaokui Xiao, Quanquan Gu, Pan Xu, 2020, COLT.

Gradient descent optimizes over-parameterized deep ReLU networks

Quanquan Gu, Difan Zou, Yuanbin Cao, 2019, Machine Learning.

Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent

Tingting Zhang, Quanquan Gu, Pan Xu, 2017, AISTATS.

Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption pdf

Xiao Zhang, Jinghui Chen, Lingxiao Wang, 2017, ArXiv.

Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method pdf

Quanquan Gu, Pan Xu, Dongruo Zhou, 2018, ArXiv.

Active Learning: A Survey

Philip S. Yu, Charu C. Aggarwal, Jiawei Han, 2014, Data Classification: Algorithms and Applications.

Robust Gaussian Graphical Model Estimation with Arbitrary Corruption

Lingxiao Wang, Quanquan Gu, Lingxiao Wang, 2017, ICML.

Self-training Converts Weak Learners to Strong Learners in Mixture Models pdf

Quanquan Gu, Spencer Frei, Difan Zou, 2021, AISTATS.

Trustworthiness analysis of sensor data in cyber-physical systems

Thomas F. La Porta, Sangkyum Kim, Jiawei Han, 2013, J. Comput. Syst. Sci..

Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes

Quanquan Gu, Wei Xiong, Chen Ye, 2022, ICML.

Benign Overfitting in Adversarially Robust Linear Classification pdf

Quanquan Gu, Yuan Cao, Jinghui Chen, 2021, UAI.

Sparse PCA with Oracle Property

Zhaoran Wang, Quanquan Gu, Han Liu, 2014, NIPS.

Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL

Quanquan Gu, Amy Zhang, Jiafan He, 2021, UAI.

Neural Contextual Bandits with UCB-based Exploration

Lihong Li, Quanquan Gu, Dongruo Zhou, 2019, ICML.

Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization

Quanquan Gu, Pan Xu, Pan Xu, 2017, NeurIPS.

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path

Quanquan Gu, Jiafan He, Dongruo Zhou, 2024, ICML.

Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation

Quanquan Gu, Dongruo Zhou, Yue Wu, 2021, AISTATS.

Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate pdf

Vladimir Braverman, Quanquan Gu, Jingfeng Wu, 2020, ICLR.

Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping

Quanquan Gu, Dongruo Zhou, Jiafan He, 2020, ICML.

Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification

Quanquan Gu, Jie Zhou, Quanquan Gu, 2009, 2009 Ninth IEEE International Conference on Data Mining.

Mixed linear modeling techniques for predicting fatalities in vehicle crashes

Quanquan Gu, Qi Tang, Lulu Ge, 2017, 2017 Systems and Information Engineering Design Symposium (SIEDS).