Quanquan Gu

460 papers A* 168A 48B 8C 3Misc 3Journal 215Unranked 13
YearRankTypeTitle / Venue / Authors
2026 J jnl
Deep Delta Learning.
CoRR
Yifan Zhang, Yifeng Liu, Mengdi Wang, Quanquan Gu
2026 J jnl
Dimension-Independent Convergence of Underdamped Langevin Monte Carlo in KL Divergence.
CoRR
Shiyuan Zhang, Qiwei Di, Xuheng Li, Quanquan Gu
2026 J jnl
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey.
ACM Comput. Surv.
Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Dhagash Mehta, Stefano Pasquali, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, Carl Yang, Liang Zhao
2026 J jnl
Multi-Step Alignment as Markov Games: An Optimistic Online Mirror Descent Approach with Convergence Guarantees.
Trans. Mach. Learn. Res.
Yongtao Wu, Luca Viano, Kimon Antonakopoulos, Yihang Chen, Zhenyu Zhu, Quanquan Gu, Volkan Cevher
2026 J jnl
Near-Optimal Regret for KL-Regularized Multi-Armed Bandits.
CoRR
Kaixuan Ji, Qingyue Zhao, Heyang Zhao, Qiwei Di, Quanquan Gu
2026 J jnl
Protein Autoregressive Modeling via Multiscale Structure Generation.
CoRR
Yanru Qu, Cheng-Yen Hsieh, Zaixiang Zheng, Ge Liu, Quanquan Gu
2026 J jnl
Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics.
CoRR
Nima Shoghi, Yuxuan Liu, Yuning Shen, Rob Brekelmans, Pan Li, Quanquan Gu
2026 J jnl
Transformers Trained via Gradient Descent Can Provably Learn a Class of Teacher Models.
CoRR
Chenyang Zhang, Qingyue Zhao, Quanquan Gu, Yuan Cao
2025 A* conf
An All-Atom Generative Model for Designing Protein Complexes.
ICML
Ruizhe Chen, Dongyu Xue, Xiangxin Zhou, Zaixiang Zheng, Xiangxiang Zeng, Quanquan Gu
2025 J jnl
An All-Atom Generative Model for Designing Protein Complexes.
CoRR
Ruizhe Chen, Dongyu Xue, Xiangxin Zhou, Zaixiang Zheng, Xiangxiang Zeng, Quanquan Gu
2025 J jnl
Best-of-Majority: Minimax-Optimal Strategy for Pass@
CoRR
Qiwei Di, Kaixuan Ji, Xuheng Li, Heyang Zhao, Quanquan Gu
2025 A* conf
Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignment.
ICML
Yifan Zhang, Ge Zhang, Yue Wu, Kangping Xu, Quanquan Gu
2025 A* conf
Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration.
ICLR
Heyang Zhao, Xingrui Yu, David Mark Bossens, Ivor W. Tsang, Quanquan Gu
2025 J jnl
Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration.
CoRR
Heyang Zhao, Xingrui Yu, David Mark Bossens, Ivor W. Tsang, Quanquan Gu
2025 J jnl
Causal Attention with Lookahead Keys.
CoRR
Zhuoqing Song, Peng Sun, Huizhuo Yuan, Quanquan Gu
2025 A* conf
Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis.
ICLR
Zikun Zhang, Zixiang Chen, Quanquan Gu
2025 A* conf
CryoFM: A Flow-based Foundation Model for Cryo-EM Densities.
ICLR
Yi Zhou, Yilai Li, Jing Yuan, Quanquan Gu
2025 A* conf
DPLM-2: A Multimodal Diffusion Protein Language Model.
ICLR
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu
2025 J jnl
Decomposed Direct Preference Optimization for Structure-Based Drug Design.
Trans. Mach. Learn. Res.
Xiwei Cheng, Xiangxin Zhou, Yuwei Yang, Yu Bao, Quanquan Gu
2025 A* conf
Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling.
ICML
Xiangxin Zhou, Mingyu Li, Yi Xiao, Jiahan Li, Dongyu Xue, Zaixiang Zheng, Jianzhu Ma, Quanquan Gu
2025 J jnl
Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling.
CoRR
Xiangxin Zhou, Mingyu Li, Yi Xiao, Jiahan Li, Dongyu Xue, Zaixiang Zheng, Jianzhu Ma, Quanquan Gu
2025 A* conf
Elucidating the Design Space of Multimodal Protein Language Models.
ICML
Cheng-Yen Hsieh, Xinyou Wang, Daiheng Zhang, Dongyu Xue, Fei Ye, Shujian Huang, Zaixiang Zheng, Quanquan Gu
2025 J jnl
Elucidating the Design Space of Multimodal Protein Language Models.
CoRR
Cheng-Yen Hsieh, Xinyou Wang, Daiheng Zhang, Dongyu Xue, Fei Ye, Shujian Huang, Zaixiang Zheng, Quanquan Gu
2025 A* conf
Energy-Weighted Flow Matching for Offline Reinforcement Learning.
ICLR
Shiyuan Zhang, Weitong Zhang, Quanquan Gu
2025 J jnl
Energy-Weighted Flow Matching for Offline Reinforcement Learning.
CoRR
Shiyuan Zhang, Weitong Zhang, Quanquan Gu
2025 J jnl
Game-Theoretic Regularized Self-Play Alignment of Large Language Models.
CoRR
Xiaohang Tang, Sangwoong Yoon, Seongho Son, Huizhuo Yuan, Quanquan Gu, Ilija Bogunovic
2025 J jnl
Global Convergence and Rich Feature Learning in
CoRR
Zixiang Chen, Greg Yang, Qingyue Zhao, Quanquan Gu
2025 A* conf
Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μ Parametrization.
ICML
Zixiang Chen, Greg Yang, Qingyue Zhao, Quanquan Gu
2025 J jnl
Group Representational Position Encoding.
CoRR
Yifan Zhang, Zixiang Chen, Yifeng Liu, Zhen Qin, Huizhuo Yuan, Kangping Xu, Yang Yuan, Quanquan Gu, Andrew Chi-Chih Yao
2025 J jnl
Guided Discrete Diffusion for Electronic Health Record Generation.
Trans. Mach. Learn. Res.
Jun Han, Zixiang Chen, Yongqian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu
2025 J jnl
Higher-order Linear Attention.
CoRR
Yifan Zhang, Zhen Qin, Quanquan Gu
2025 A* conf
LLaVA-Critic: Learning to Evaluate Multimodal Models.
CVPR
Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, Chunyuan Li
2025 A* conf
Logarithmic Regret for Online KL-Regularized Reinforcement Learning.
ICML
Heyang Zhao, Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang
2025 J jnl
Logarithmic Regret for Online KL-Regularized Reinforcement Learning.
CoRR
Heyang Zhao, Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang
2025 J jnl
MARS-M: When Variance Reduction Meets Matrices.
CoRR
Yifeng Liu, Angela Yuan, Quanquan Gu
2025 A* conf
MARS: Unleashing the Power of Variance Reduction for Training Large Models.
ICML
Huizhuo Yuan, Yifeng Liu, Shuang Wu, Xun Zhou, Quanquan Gu
2025 A* conf
Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance.
ICML
Linxi Zhao, Yihe Deng, Weitong Zhang, Quanquan Gu
2025 J jnl
Multi-Step Alignment as Markov Games: An Optimistic Online Gradient Descent Approach with Convergence Guarantees.
CoRR
Yongtao Wu, Luca Viano, Yihang Chen, Zhenyu Zhu, Kimon Antonakopoulos, Quanquan Gu, Volkan Cevher
2025 A* conf
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback.
ICML
Qiwei Di, Jiafan He, Quanquan Gu
2025 J jnl
Nearly Optimal Sample Complexity of Offline KL-Regularized Contextual Bandits under Single-Policy Concentrability.
CoRR
Qingyue Zhao, Kaixuan Ji, Heyang Zhao, Tong Zhang, Quanquan Gu
2025 J jnl
On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning.
CoRR
Yifan Zhang, Yifeng Liu, Huizhuo Yuan, Yang Yuan, Quanquan Gu, Andrew C. Yao
2025 J jnl
On the Limits of Test-Time Compute: Sequential Reward Filtering for Better Inference.
CoRR
Yue Yu, Qiwei Di, Quanquan Gu, Dongruo Zhou
2025 A conf
On the Power of Multitask Representation Learning with Gradient Descent.
AISTATS
Qiaobo Li, Zixiang Chen, Yihe Deng, Yiwen Kou, Yuan Cao, Quanquan Gu
2025 A* conf
ProteinBench: A Holistic Evaluation of Protein Foundation Models.
ICLR
Fei Ye, Zaixiang Zheng, Dongyu Xue, Yuning Shen, Lihao Wang, Yiming Ma, Yan Wang, Xinyou Wang, Xiangxin Zhou, Quanquan Gu
2025 A* conf
Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity.
ICML
Tao Jin, Yue Wu, Quanquan Gu, Farzad Farnoud
2025 J jnl
Reinforcement Learning from Human Feedback with Active Queries.
Trans. Mach. Learn. Res.
Kaixuan Ji, Jiafan He, Quanquan Gu
2025 J jnl
Robust Layerwise Scaling Rules by Proper Weight Decay Tuning.
CoRR
Zhiyuan Fan, Yifeng Liu, Qingyue Zhao, Angela Yuan, Quanquan Gu
2025 A* conf
Self-Play Preference Optimization for Language Model Alignment.
ICLR
Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, Quanquan Gu
2025 J jnl
Simultaneous Modeling of Protein Conformation and Dynamics via Autoregression.
CoRR
Yuning Shen, Lihao Wang, Huizhuo Yuan, Yan Wang, Bangji Yang, Quanquan Gu
2025 J jnl
SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving.
CoRR
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, Ying Sheng, Zhijiang Guo, Hongxia Yang, Bei Yu, Lingpeng Kong, Quanquan Gu, Ngai Wong
2025 J jnl
Tensor Product Attention Is All You Need.
CoRR
Yifan Zhang, Yifeng Liu, Huizhuo Yuan, Zhen Qin, Yang Yuan, Quanquan Gu, Andrew Chi-Chih Yao
2025 J jnl
Understanding SGD with Exponential Moving Average: A Case Study in Linear Regression.
CoRR
Xuheng Li, Quanquan Gu
2025 A* conf
Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers.
ICLR
Runjia Li, Qiwei Di, Quanquan Gu
2025 J jnl
Variance-Aware Feel-Good Thompson Sampling for Contextual Bandits.
CoRR
Xuheng Li, Quanquan Gu
2025 J jnl
Variance-Dependent Regret Lower Bounds for Contextual Bandits.
CoRR
Jiafan He, Quanquan Gu
2024 A* conf
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation.
NeurIPS
Heyang Zhao, Jiafan He, Quanquan Gu
2024 J jnl
Accelerated Preference Optimization for Large Language Model Alignment.
CoRR
Jiafan He, Huizhuo Yuan, Quanquan Gu
2024 A* conf
Achieving Constant Regret in Linear Markov Decision Processes.
NeurIPS
Weitong Zhang, Zhiyuan Fan, Jiafan He, Quanquan Gu
2024 A* conf
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization.
NeurIPS
Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, Quanquan Gu
2024 J jnl
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization.
CoRR
Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, Quanquan Gu
2024 A* conf
Borda Regret Minimization for Generalized Linear Dueling Bandits.
ICML
Yue Wu, Tao Jin, Qiwei Di, Hao Lou, Farzad Farnoud, Quanquan Gu
2024 A* conf
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems.
WWW
Zijie Huang, Jeehyun Hwang, Junkai Zhang, Jinwoo Baik, Weitong Zhang, Dominik Wodarz, Yizhou Sun, Quanquan Gu, Wei Wang
2024 J jnl
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems.
CoRR
Zijie Huang, Jeehyun Hwang, Junkai Zhang, Jinwoo Baik, Weitong Zhang, Dominik Wodarz, Yizhou Sun, Quanquan Gu, Wei Wang
2024 J jnl
Challenges of COVID-19 Case Forecasting in the US, 2020-2021.
PLoS Comput. Biol.
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, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph Chadi Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren A. Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan M. Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson
2024 J jnl
CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing.
CoRR
Chen Yang, Chenyang Zhao, Quanquan Gu, Dongruo Zhou
2024 J jnl
Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis.
CoRR
Zikun Zhang, Zixiang Chen, Quanquan Gu
2024 J jnl
CryoFM: A Flow-based Foundation Model for Cryo-EM Densities.
CoRR
Yi Zhou, Yilai Li, Jing Yuan, Quanquan Gu
2024 J jnl
DPLM-2: A Multimodal Diffusion Protein Language Model.
CoRR
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu
2024 J jnl
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design.
CoRR
Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu
2024 A* conf
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization.
ICLR
Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, Quanquan Gu
2024 J jnl
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization.
CoRR
Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, Quanquan Gu
2024 J jnl
Decomposed Direct Preference Optimization for Structure-Based Drug Design.
CoRR
Xiwei Cheng, Xiangxin Zhou, Yuwei Yang, Yu Bao, Quanquan Gu
2024 A* conf
Diffusion Language Models Are Versatile Protein Learners.
ICML
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu
2024 J jnl
Diffusion Language Models Are Versatile Protein Learners.
CoRR
Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu
2024 A* conf
Enhancing Large Vision Language Models with Self-Training on Image Comprehension.
NeurIPS
Yihe Deng, Pan Lu, Fan Yin, Ziniu Hu, Sheng Shen, Quanquan Gu, James Y. Zou, Kai-Wei Chang, Wei Wang
2024 J jnl
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization.
CoRR
Guanlin Liu, Kaixuan Ji, Renjie Zheng, Zheng Wu, Chen Dun, Quanquan Gu, Lin Yan
2024 A* conf
Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time.
NeurIPS
Zixiang Chen, Huizhuo Yuan, Yongqian Li, Yiwen Kou, Junkai Zhang, Quanquan Gu
2024 A* conf
Feel-Good Thompson Sampling for Contextual Dueling Bandits.
ICML
Xuheng Li, Heyang Zhao, Quanquan Gu
2024 J jnl
Feel-Good Thompson Sampling for Contextual Dueling Bandits.
CoRR
Xuheng Li, Heyang Zhao, Quanquan Gu
2024 J jnl
General Preference Modeling with Preference Representations for Aligning Language Models.
CoRR
Yifan Zhang, Ge Zhang, Yue Wu, Kangping Xu, Quanquan Gu
2024 J jnl
Guided Discrete Diffusion for Electronic Health Record Generation.
CoRR
Zixiang Chen, Jun Han, Yongqian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu
2024 A* conf
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs.
ICLR
Kaixuan Ji, Qingyue Zhao, Jiafan He, Weitong Zhang, Quanquan Gu
2024 A* conf
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
ICLR
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
2024 J jnl
LLaVA-Critic: Learning to Evaluate Multimodal Models.
CoRR
Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, Chunyuan Li
2024 A* conf
Large Language Models Can Be Contextual Privacy Protection Learners.
EMNLP
Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
2024 J jnl
MARS: Unleashing the Power of Variance Reduction for Training Large Models.
CoRR
Huizhuo Yuan, Yifeng Liu, Shuang Wu, Xun Zhou, Quanquan Gu
2024 A* conf
Matching the Statistical Query Lower Bound for k-Sparse Parity Problems with Sign Stochastic Gradient Descent.
NeurIPS
Yiwen Kou, Zixiang Chen, Quanquan Gu, Sham M. Kakade
2024 J jnl
Matching the Statistical Query Lower Bound for k-sparse Parity Problems with Stochastic Gradient Descent.
CoRR
Yiwen Kou, Zixiang Chen, Quanquan Gu, Sham M. Kakade
2024 J jnl
Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance.
CoRR
Linxi Zhao, Yihe Deng, Weitong Zhang, Quanquan Gu
2024 J jnl
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path.
CoRR
Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu
2024 J jnl
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback.
CoRR
Qiwei Di, Jiafan He, Quanquan Gu
2024 J jnl
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization.
Trans. Mach. Learn. Res.
Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu
2024 A* conf
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning.
ICLR
Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu
2024 A* conf
Position: TrustLLM: Trustworthiness in Large Language Models.
ICML
Yue Huang, Lichao Sun, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Hanchi Sun, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao
2024 J jnl
Pre-trained Hypergraph Convolutional Neural Networks with Self-supervised Learning.
Trans. Mach. Learn. Res.
Yihe Deng, Ruochi Zhang, Pan Xu, Jian Ma, Quanquan Gu
2024 A* conf
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models.
ICML
Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, Quanquan Gu
2024 J jnl
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models.
CoRR
Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, Quanquan Gu
2024 J jnl
ProteinBench: A Holistic Evaluation of Protein Foundation Models.
CoRR
Fei Ye, Zaixiang Zheng, Dongyu Xue, Yuning Shen, Lihao Wang, Yiming Ma, Yan Wang, Xinyou Wang, Xiangxin Zhou, Quanquan Gu
2024 J jnl
ProteinWeaver: A Divide-and-Assembly Approach for Protein Backbone Design.
CoRR
Yiming Ma, Fei Ye, Yi Zhou, Zaixiang Zheng, Dongyu Xue, Quanquan Gu
2024 A conf
Pure Exploration in Asynchronous Federated Bandits.
UAI
Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu, Huazheng Wang
2024 J jnl
Reinforcement Learning from Human Feedback with Active Queries.
CoRR
Kaixuan Ji, Jiafan He, Quanquan Gu
2024 J jnl
Relative-Translation Invariant Wasserstein Distance.
CoRR
Binshuai Wang, Qiwei Di, Ming Yin, Mengdi Wang, Quanquan Gu, Peng Wei
2024 A* conf
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression.
ICLR
Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu
2024 A* conf
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.
ICML
Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu
2024 J jnl
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models.
CoRR
Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu
2024 J jnl
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation.
CoRR
Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu
2024 A* conf
Self-Play Fine-tuning of Diffusion Models for Text-to-image Generation.
NeurIPS
Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu
2024 J jnl
Self-Play Preference Optimization for Language Model Alignment.
CoRR
Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, Quanquan Gu
2024 J jnl
Settling Constant Regrets in Linear Markov Decision Processes.
CoRR
Weitong Zhang, Zhiyuan Fan, Jiafan He, Quanquan Gu
2024 J jnl
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF.
CoRR
Heyang Zhao, Chenlu Ye, Quanquan Gu, Tong Zhang
2024 A* conf
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption.
ICML
Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang
2024 J jnl
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption.
CoRR
Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang
2024 J jnl
Towards Simple and Provable Parameter-Free Adaptive Gradient Methods.
CoRR
Yuanzhe Tao, Huizhuo Yuan, Xun Zhou, Yuan Cao, Quanquan Gu
2024 J jnl
TrustLLM: Trustworthiness in Large Language Models.
CoRR
Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, John C. Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yue Zhao
2024 A* conf
Uncertainty-Aware Reward-Free Exploration with General Function Approximation.
ICML
Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu
2024 J jnl
Uncertainty-Aware Reward-Free Exploration with General Function Approximation.
CoRR
Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu
2024 A* conf
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP.
ICLR
Zixiang Chen, Yihe Deng, Yuanzhi Li, Quanquan Gu
2024 J jnl
Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers.
CoRR
Runjia Li, Qiwei Di, Quanquan Gu
2024 A* conf
Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits.
ICLR
Qiwei Di, Tao Jin, Yue Wu, Heyang Zhao, Farzad Farnoud, Quanquan Gu
2023 A* conf
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning.
ICLR
Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan
2023 J jnl
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation.
CoRR
Heyang Zhao, Jiafan He, Quanquan Gu
2023 J jnl
Benign Overfitting for Two-layer ReLU Networks.
CoRR
Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu
2023 A conf
Benign Overfitting in Adversarially Robust Linear Classification.
UAI
Jinghui Chen, Yuan Cao, Quanquan Gu
2023 A* conf
Benign Overfitting in Two-layer ReLU Convolutional Neural Networks.
ICML
Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu
2023 J jnl
Benign Overfitting of Constant-Stepsize SGD for Linear Regression.
J. Mach. Learn. Res.
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023 J jnl
Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models.
CoRR
Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun Li, Hejie Cui, Xuchao Zhang, Tianjiao Zhao, Amit Panalkar, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Carl Yang, Liang Zhao
2023 J jnl
Borda Regret Minimization for Generalized Linear Dueling Bandits.
CoRR
Yue Wu, Tao Jin, Hao Lou, Farzad Farnoud, Quanquan Gu
2023 A* conf
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation.
ICML
Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu
2023 J jnl
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation.
CoRR
Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu
2023 A* conf
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes.
ICML
Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang
2023 A* conf
Corruption-Robust Offline Reinforcement Learning with General Function Approximation.
NeurIPS
Chenlu Ye, Rui Yang, Quanquan Gu, Tong Zhang
2023 J jnl
Corruption-Robust Offline Reinforcement Learning with General Function Approximation.
CoRR
Chenlu Ye, Rui Yang, Quanquan Gu, Tong Zhang
2023 A* conf
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design.
ICML
Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu
2023 J jnl
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning.
CoRR
Jiasheng Ye, Zaixiang Zheng, Yu Bao, Lihua Qian, Quanquan Gu
2023 A conf
Efficient Privacy-Preserving Stochastic Nonconvex Optimization.
UAI
Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu
2023 J jnl
Fast Sampling via De-randomization for Discrete Diffusion Models.
CoRR
Zixiang Chen, Huizhuo Yuan, Yongqian Li, Yiwen Kou, Junkai Zhang, Quanquan Gu
2023 A* conf
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron.
ICML
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023 J jnl
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs.
CoRR
Kaixuan Ji, Qingyue Zhao, Jiafan He, Weitong Zhang, Quanquan Gu
2023 A* conf
How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study.
ICLR
Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu
2023 J jnl
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
CoRR
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett
2023 A* conf
Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data.
NeurIPS
Yiwen Kou, Zixiang Chen, Quanquan Gu
2023 J jnl
Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data.
CoRR
Yiwen Kou, Zixiang Chen, Quanquan Gu
2023 J jnl
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples.
CoRR
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2023 A* conf
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path.
ICML
Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu
2023 A* conf
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes.
ICML
Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu
2023 A* conf
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization.
ICML
Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan
2023 A* conf
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits.
ICML
Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu
2023 J jnl
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits.
CoRR
Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu
2023 A* conf
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure.
NeurIPS
Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S. Du
2023 A* conf
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs.
ICML
Junkai Zhang, Weitong Zhang, Quanquan Gu
2023 J jnl
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs.
CoRR
Junkai Zhang, Weitong Zhang, Quanquan Gu
2023 A* conf
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits.
ICML
Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu
2023 A* conf
Personalized Federated Learning under Mixture of Distributions.
ICML
Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng
2023 J jnl
Personalized Federated Learning under Mixture of Distributions.
CoRR
Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng
2023 J jnl
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning.
CoRR
Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu
2023 A conf
Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL.
UAI
Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu
2023 J jnl
Pure Exploration in Asynchronous Federated Bandits.
CoRR
Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu, Huazheng Wang
2023 J jnl
Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves.
CoRR
Yihe Deng, Weitong Zhang, Zixiang Chen, Quanquan Gu
2023 J jnl
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression.
CoRR
Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu
2023 A* conf
Robust Learning with Progressive Data Expansion Against Spurious Correlation.
NeurIPS
Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu
2023 J jnl
Robust Learning with Progressive Data Expansion Against Spurious Correlation.
CoRR
Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu
2023 J jnl
Sparse PCA with Oracle Property.
CoRR
Quanquan Gu, Zhaoran Wang, Han Liu
2023 A* conf
Structure-informed Language Models Are Protein Designers.
ICML
Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu
2023 J jnl
Structure-informed Language Models Are Protein Designers.
CoRR
Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu
2023 A* conf
The Benefits of Mixup for Feature Learning.
ICML
Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
2023 J jnl
The Benefits of Mixup for Feature Learning.
CoRR
Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
2023 A* conf
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks.
COLT
Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu
2023 J jnl
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks.
CoRR
Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu
2023 A* conf
Understanding Train-Validation Split in Meta-Learning with Neural Networks.
ICLR
Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu
2023 J jnl
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP.
CoRR
Zixiang Chen, Yihe Deng, Yuanzhi Li, Quanquan Gu
2023 A* conf
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization.
ICLR
Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
2023 A conf
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension.
UAI
Yue Wu, Jiafan He, Quanquan Gu
2023 J jnl
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension.
CoRR
Yue Wu, Jiafan He, Quanquan Gu
2023 J jnl
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits.
CoRR
Qiwei Di, Tao Jin, Yue Wu, Heyang Zhao, Farzad Farnoud, Quanquan Gu
2023 A* conf
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency.
COLT
Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
2023 J jnl
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency.
CoRR
Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
2023 A* conf
Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
NeurIPS
Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu
2023 J jnl
Why Does Sharpness-Aware Minimization Generalize Better Than SGD?
CoRR
Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu
2022 J jnl
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning.
CoRR
Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan
2022 A* conf
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits.
NeurIPS
Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu
2022 J jnl
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits.
CoRR
Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu
2022 A* conf
Active Ranking without Strong Stochastic Transitivity.
NeurIPS
Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud
2022 A conf
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons.
AISTATS
Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu
2022 B conf
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games.
ALT
Zixiang Chen, Dongruo Zhou, Quanquan Gu
2022 J jnl
Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds.
CoRR
Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu
2022 A* conf
Benign Overfitting in Two-layer Convolutional Neural Networks.
NeurIPS
Yuan Cao, Zixiang Chen, Misha Belkin, Quanquan Gu
2022 J jnl
Benign Overfitting in Two-layer Convolutional Neural Networks.
CoRR
Yuan Cao, Zixiang Chen, Mikhail Belkin, Quanquan Gu
2022 A* conf
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs.
NeurIPS
Dongruo Zhou, Quanquan Gu
2022 J jnl
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs.
CoRR
Dongruo Zhou, Quanquan Gu
2022 J jnl
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes.
CoRR
Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang
2022 A* conf
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization.
ICML
Dongruo Zhou, Quanquan Gu
2022 A* conf
Efficient Robust Training via Backward Smoothing.
AAAI
Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu, Jingjing Liu
2022 B conf
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima.
ALT
Zixiang Chen, Dongruo Zhou, Quanquan Gu
2022 A* conf
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression.
ICML
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022 J jnl
Learning Contextual Bandits Through Perturbed Rewards.
CoRR
Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang
2022 A* conf
Learning Neural Contextual Bandits through Perturbed Rewards.
ICLR
Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang
2022 A* conf
Learning Stochastic Shortest Path with Linear Function Approximation.
ICML
Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu
2022 A* conf
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium.
NeurIPS
Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
2022 J jnl
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium.
CoRR
Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan
2022 C conf
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes.
ACML
Chonghua Liao, Jiafan He, Quanquan Gu
2022 C conf
Machine-Learning-based Predictive Control of Nonlinear Processes with Uncertainty.
ACC
Zhe Wu, Aisha Alnajdi, Quanquan Gu, Panagiotis D. Christofides
2022 A conf
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs.
AISTATS
Jiafan He, Dongruo Zhou, Quanquan Gu
2022 A conf
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation.
AISTATS
Yue Wu, Dongruo Zhou, Quanquan Gu
2022 J jnl
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes.
CoRR
Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu
2022 A* conf
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions.
NeurIPS
Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
2022 J jnl
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions.
CoRR
Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu
2022 A* conf
Neural Contextual Bandits with Deep Representation and Shallow Exploration.
ICLR
Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu
2022 A* conf
On the Convergence of Certified Robust Training with Interval Bound Propagation.
ICLR
Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh
2022 J jnl
On the Convergence of Certified Robust Training with Interval Bound Propagation.
CoRR
Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh
2022 A* conf
On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs.
ICML
Yuanzhou Chen, Jiafan He, Quanquan Gu
2022 A* conf
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime.
NeurIPS
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022 J jnl
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime.
CoRR
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022 A conf
Self-training Converts Weak Learners to Strong Learners in Mixture Models.
AISTATS
Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu
2022 A* conf
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift.
NeurIPS
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022 J jnl
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift.
CoRR
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2022 J jnl
Towards Understanding Mixture of Experts in Deep Learning.
CoRR
Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li
2022 A* conf
Towards Understanding the Mixture-of-Experts Layer in Deep Learning.
NeurIPS
Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li
2021 J jnl
Adaptive Differentially Private Empirical Risk Minimization.
CoRR
Xiaoxia Wu, Lingxiao Wang, Irina Cristali, Quanquan Gu, Rebecca Willett
2021 J jnl
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons.
CoRR
Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu
2021 A* conf
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins.
ICML
Spencer Frei, Yuan Cao, Quanquan Gu
2021 J jnl
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation.
CoRR
Zixiang Chen, Dongruo Zhou, Quanquan Gu
2021 A* conf
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits.
ICML
Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu
2021 J jnl
Batched Neural Bandits.
CoRR
Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab S. Mirrokni, Dongruo Zhou
2021 J jnl
Benign Overfitting in Adversarially Robust Linear Classification.
CoRR
Jinghui Chen, Yuan Cao, Quanquan Gu
2021 A* conf
Benign Overfitting of Constant-Stepsize SGD for Linear Regression.
COLT
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021 J jnl
Benign Overfitting of Constant-Stepsize SGD for Linear Regression.
CoRR
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021 A* conf
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate.
ICLR
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
2021 A* conf
Do Wider Neural Networks Really Help Adversarial Robustness?
NeurIPS
Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu
2021 A* conf
Double Explore-then-Commit: Asymptotic Optimality and Beyond.
COLT
Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu
2021 A* conf
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks.
NeurIPS
Hanxun Huang, Yisen Wang, Sarah M. Erfani, Quanquan Gu, James Bailey, Xingjun Ma
2021 J jnl
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks.
CoRR
Hanxun Huang, Yisen Wang, Sarah Monazam Erfani, Quanquan Gu, James Bailey, Xingjun Ma
2021 A conf
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling.
UAI
Difan Zou, Pan Xu, Quanquan Gu
2021 J jnl
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima.
CoRR
Zixiang Chen, Dongruo Zhou, Quanquan Gu
2021 A* conf
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
ICLR
Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
2021 A* conf
Iterative Teacher-Aware Learning.
NeurIPS
Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu
2021 J jnl
Iterative Teacher-Aware Learning.
CoRR
Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu
2021 J jnl
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo.
SIAM J. Sci. Comput.
Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher
2021 J jnl
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression.
CoRR
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade
2021 J jnl
Learning Stochastic Shortest Path with Linear Function Approximation.
CoRR
Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu
2021 J jnl
Linear Contextual Bandits with Adversarial Corruptions.
CoRR
Heyang Zhao, Dongruo Zhou, Quanquan Gu
2021 J jnl
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes.
CoRR
Chonghua Liao, Jiafan He, Quanquan Gu
2021 A* conf
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation.
ICML
Jiafan He, Dongruo Zhou, Quanquan Gu
2021 A* conf
MOTS: Minimax Optimal Thompson Sampling.
ICML
Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu
2021 J jnl
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation.
CoRR
Yue Wu, Dongruo Zhou, Quanquan Gu
2021 A* conf
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs.
NeurIPS
Jiafan He, Dongruo Zhou, Quanquan Gu
2021 A* conf
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes.
COLT
Dongruo Zhou, Quanquan Gu, Csaba Szepesvári
2021 J jnl
Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation.
CoRR
Jiafan He, Dongruo Zhou, Quanquan Gu
2021 A* conf
Neural Thompson Sampling.
ICLR
Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu
2021 J jnl
On the Convergence and Robustness of Adversarial Training.
CoRR
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu
2021 A* conf
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients.
ICML
Difan Zou, Quanquan Gu
2021 A* conf
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise.
ICML
Spencer Frei, Yuan Cao, Quanquan Gu
2021 J jnl
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise.
CoRR
Spencer Frei, Yuan Cao, Quanquan Gu
2021 A* conf
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise.
ICML
Difan Zou, Spencer Frei, Quanquan Gu
2021 J jnl
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise.
CoRR
Difan Zou, Spencer Frei, Quanquan Gu
2021 A* conf
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping.
ICML
Dongruo Zhou, Jiafan He, Quanquan Gu
2021 J jnl
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints.
CoRR
Tianhao Wang, Dongruo Zhou, Quanquan Gu
2021 A* conf
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints.
NeurIPS
Tianhao Wang, Dongruo Zhou, Quanquan Gu
2021 J jnl
Provably Efficient Representation Learning in Low-rank Markov Decision Processes.
CoRR
Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu
2021 A* conf
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent.
NeurIPS
Spencer Frei, Quanquan Gu
2021 J jnl
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent.
CoRR
Spencer Frei, Quanquan Gu
2021 A* conf
Pure Exploration in Kernel and Neural Bandits.
NeurIPS
Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak
2021 J jnl
Pure Exploration in Kernel and Neural Bandits.
CoRR
Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert D. Nowak
2021 J jnl
Revisiting Membership Inference Under Realistic Assumptions.
Proc. Priv. Enhancing Technol.
Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans
2021 A* conf
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation.
NeurIPS
Weitong Zhang, Dongruo Zhou, Quanquan Gu
2021 J jnl
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation.
CoRR
Weitong Zhang, Dongruo Zhou, Quanquan Gu
2021 A* conf
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures.
NeurIPS
Yuan Cao, Quanquan Gu, Mikhail Belkin
2021 J jnl
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures.
CoRR
Yuan Cao, Quanquan Gu, Mikhail Belkin
2021 J jnl
Self-training Converts Weak Learners to Strong Learners in Mixture Models.
CoRR
Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu
2021 A* conf
The Benefits of Implicit Regularization from SGD in Least Squares Problems.
NeurIPS
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
2021 J jnl
The Benefits of Implicit Regularization from SGD in Least Squares Problems.
CoRR
Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade
2021 A* conf
Towards Understanding the Spectral Bias of Deep Learning.
IJCAI
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
2021 J jnl
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization.
CoRR
Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu
2021 A* conf
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation.
NeurIPS
Jiafan He, Dongruo Zhou, Quanquan Gu
2021 J jnl
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation.
CoRR
Jiafan He, Dongruo Zhou, Quanquan Gu
2021 A* conf
Variance-Aware Off-Policy Evaluation with Linear Function Approximation.
NeurIPS
Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu
2021 J jnl
Variance-Aware Off-Policy Evaluation with Linear Function Approximation.
CoRR
Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu
2021 — conf
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks.
NAACL-HLT
Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang
2020 J jnl
A Finite Time Analysis of Two Time-Scale Actor Critic Methods.
CoRR
Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu
2020 A* conf
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation.
ICML
Pan Xu, Quanquan Gu
2020 A* conf
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods.
NeurIPS
Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu
2020 A* conf
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks.
AAAI
Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu
2020 A* conf
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks.
NeurIPS
Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang
2020 A* conf
A Knowledge Transfer Framework for Differentially Private Sparse Learning.
AAAI
Lingxiao Wang, Quanquan Gu
2020 A conf
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization.
AISTATS
Dongruo Zhou, Yuan Cao, Quanquan Gu
2020 J jnl
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins.
CoRR
Spencer Frei, Yuan Cao, Quanquan Gu
2020 A* conf
Agnostic Learning of a Single Neuron with Gradient Descent.
NeurIPS
Spencer Frei, Yuan Cao, Quanquan Gu
2020 J jnl
Agnostic Learning of a Single Neuron with Gradient Descent.
CoRR
Spencer Frei, Yuan Cao, Quanquan Gu
2020 A* conf
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks.
IJCAI
Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu
2020 — conf
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM.
MSML
Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher
2020 J jnl
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate.
CoRR
Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu
2020 J jnl
Does Network Width Really Help Adversarial Robustness?
CoRR
Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu
2020 J jnl
Double Explore-then-Commit: Asymptotic Optimality and Beyond.
CoRR
Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu
2020 J jnl
Efficient Robust Training via Backward Smoothing.
CoRR
Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu, Jingjing Liu
2020 J jnl
Exploring Private Federated Learning with Laplacian Smoothing.
CoRR
Zhicong Liang, Bao Wang, Quanquan Gu, Stanley J. Osher, Yuan Yao
2020 J jnl
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling.
CoRR
Difan Zou, Pan Xu, Quanquan Gu
2020 A* conf
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks.
AAAI
Yuan Cao, Quanquan Gu
2020 J jnl
Gradient descent optimizes over-parameterized deep ReLU networks.
Mach. Learn.
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
2020 A* conf
Improving Adversarial Robustness Requires Revisiting Misclassified Examples.
ICLR
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu
2020 A* conf
Improving Neural Language Generation with Spectrum Control.
ICLR
Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu
2020 — conf
Is neuron coverage a meaningful measure for testing deep neural networks?
ESEC/SIGSOFT FSE
Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu, Miryung Kim
2020 J jnl
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation.
CoRR
Jiafan He, Dongruo Zhou, Quanquan Gu
2020 J jnl
MOTS: Minimax Optimal Thompson Sampling.
CoRR
Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu
2020 J jnl
Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds.
CoRR
Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang
2020 J jnl
Minimax Optimal Reinforcement Learning for Discounted MDPs.
CoRR
Jiafan He, Dongruo Zhou, Quanquan Gu
2020 J jnl
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes.
CoRR
Dongruo Zhou, Quanquan Gu, Csaba Szepesvári
2020 J jnl
Neural Contextual Bandits with Deep Representation and Shallow Exploration.
CoRR
Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu
2020 A* conf
Neural Contextual Bandits with UCB-based Exploration.
ICML
Dongruo Zhou, Lihong Li, Quanquan Gu
2020 J jnl
Neural Thompson Sampling.
CoRR
Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu
2020 A* conf
On the Global Convergence of Training Deep Linear ResNets.
ICLR
Difan Zou, Philip M. Long, Quanquan Gu
2020 J jnl
On the Global Convergence of Training Deep Linear ResNets.
CoRR
Difan Zou, Philip M. Long, Quanquan Gu
2020 A* conf
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers.
ICML
Yonatan Dukler, Quanquan Gu, Guido Montúfar
2020 J jnl
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers.
CoRR
Yonatan Dukler, Quanquan Gu, Guido Montúfar
2020 J jnl
Provable Multi-Objective Reinforcement Learning with Generative Models.
CoRR
Dongruo Zhou, Jiahao Chen, Quanquan Gu
2020 J jnl
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping.
CoRR
Dongruo Zhou, Jiafan He, Quanquan Gu
2020 A* conf
Rank Aggregation via Heterogeneous Thurstone Preference Models.
AAAI
Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud
2020 A* conf
RayS: A Ray Searching Method for Hard-label Adversarial Attack.
KDD
Jinghui Chen, Quanquan Gu
2020 J jnl
RayS: A Ray Searching Method for Hard-label Adversarial Attack.
CoRR
Jinghui Chen, Quanquan Gu
2020 J jnl
Revisiting Membership Inference Under Realistic Assumptions.
CoRR
Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
2020 A* conf
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction.
ICLR
Pan Xu, Felicia Gao, Quanquan Gu
2020 J jnl
Stochastic Nested Variance Reduction for Nonconvex Optimization.
J. Mach. Learn. Res.
Dongruo Zhou, Pan Xu, Quanquan Gu
2020 A conf
Stochastic Recursive Variance-Reduced Cubic Regularization Methods.
AISTATS
Dongruo Zhou, Quanquan Gu
2020 A conf
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models.
AISTATS
Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans
2020 J jnl
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models.
CoRR
Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans
2019 J jnl
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation.
CoRR
Pan Xu, Quanquan Gu
2019 J jnl
A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks.
CoRR
Yuan Cao, Quanquan Gu
2019 J jnl
A Knowledge Transfer Framework for Differentially Private Sparse Learning.
CoRR
Lingxiao Wang, Quanquan Gu
2019 A* conf
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks.
NeurIPS
Spencer Frei, Yuan Cao, Quanquan Gu
2019 J jnl
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks.
CoRR
Spencer Frei, Yuan Cao, Quanquan Gu
2019 A* conf
An Improved Analysis of Training Over-parameterized Deep Neural Networks.
NeurIPS
Difan Zou, Quanquan Gu
2019 J jnl
An Improved Analysis of Training Over-parameterized Deep Neural Networks.
CoRR
Difan Zou, Quanquan Gu
2019 A conf
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient.
UAI
Pan Xu, Felicia Gao, Quanquan Gu
2019 J jnl
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient.
CoRR
Pan Xu, Felicia Gao, Quanquan Gu
2019 J jnl
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM.
CoRR
Bao Wang, Quanquan Gu, March Boedihardjo, Farzin Barekat, Stanley J. Osher
2019 A* conf
Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning.
IJCAI
Lingxiao Wang, Quanquan Gu
2019 J jnl
Efficient Privacy-Preserving Nonconvex Optimization.
CoRR
Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu
2019 A* conf
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks.
NeurIPS
Yuan Cao, Quanquan Gu
2019 J jnl
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks.
CoRR
Yuan Cao, Quanquan Gu
2019 J jnl
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
CoRR
Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu
2019 J jnl
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo.
CoRR
Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher
2019 A* conf
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks.
NeurIPS
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
2019 J jnl
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks.
CoRR
Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu
2019 A conf
Learning One-hidden-layer ReLU Networks via Gradient Descent.
AISTATS
Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu
2019 A* conf
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization.
ICML
Dongruo Zhou, Quanquan Gu
2019 J jnl
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization.
CoRR
Dongruo Zhou, Quanquan Gu
2019 J jnl
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration.
CoRR
Dongruo Zhou, Lihong Li, Quanquan Gu
2019 A* conf
On the Convergence and Robustness of Adversarial Training.
ICML
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu
2019 J jnl
Rank Aggregation via Heterogeneous Thurstone Preference Models.
CoRR
Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud
2019 J jnl
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction.
CoRR
Pan Xu, Felicia Gao, Quanquan Gu
2019 A conf
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics.
AISTATS
Difan Zou, Pan Xu, Quanquan Gu
2019 A* conf
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction.
NeurIPS
Difan Zou, Pan Xu, Quanquan Gu
2019 J jnl
Stochastic Recursive Variance-Reduced Cubic Regularization Methods.
CoRR
Dongruo Zhou, Quanquan Gu
2019 J jnl
Stochastic Variance-Reduced Cubic Regularization Methods.
J. Mach. Learn. Res.
Dongruo Zhou, Pan Xu, Quanquan Gu
2019 A* conf
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks.
NeurIPS
Yuan Cao, Quanquan Gu
2019 J jnl
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks.
CoRR
Yuan Cao, Quanquan Gu
2019 J jnl
Towards Understanding the Spectral Bias of Deep Learning.
CoRR
Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu
2018 J jnl
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks.
CoRR
Jinghui Chen, Jinfeng Yi, Quanquan Gu
2018 A* conf
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery.
ICML
Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu
2018 A conf
A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery.
AISTATS
Xiao Zhang, Lingxiao Wang, Quanquan Gu
2018 A conf
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms.
AISTATS
Pan Xu, Tianhao Wang, Quanquan Gu
2018 J jnl
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks.
CoRR
Jinghui Chen, Quanquan Gu
2018 A* conf
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions.
ICML
Pan Xu, Tianhao Wang, Quanquan Gu
2018 B conf
Continuous-Trait Probabilistic Model for Comparing Multi-species Functional Genomic Data.
RECOMB
Yang Yang, Quanquan Gu, Takayo Sasaki, Julianna Crivello, Rachel O'Neill, David M. Gilbert, Jian Ma
2018 A* conf
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization.
ICML
Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu
2018 — conf
Differentially Private Hypothesis Transfer Learning.
ECML/PKDD (2)
Yang Wang, Quanquan Gu, Donald E. Brown
2018 A* conf
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization.
NeurIPS
Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu
2018 A* conf
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow.
ICML
Xiao Zhang, Simon S. Du, Quanquan Gu
2018 J jnl
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow.
CoRR
Xiao Zhang, Simon S. Du, Quanquan Gu
2018 J jnl
Finding Local Minima via Stochastic Nested Variance Reduction.
CoRR
Dongruo Zhou, Pan Xu, Quanquan Gu
2018 A* conf
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization.
NeurIPS
Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu
2018 J jnl
Learning One-hidden-layer ReLU Networks via Gradient Descent.
CoRR
Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu
2018 J jnl
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization.
CoRR
Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu
2018 J jnl
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method.
CoRR
Dongruo Zhou, Pan Xu, Quanquan Gu
2018 J jnl
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks.
CoRR
Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu
2018 A* conf
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization.
NeurIPS
Dongruo Zhou, Pan Xu, Quanquan Gu
2018 J jnl
Stochastic Nested Variance Reduction for Nonconvex Optimization.
CoRR
Dongruo Zhou, Pan Xu, Quanquan Gu
2018 A* conf
Stochastic Variance-Reduced Cubic Regularized Newton Method.
ICML
Dongruo Zhou, Pan Xu, Quanquan Gu
2018 J jnl
Stochastic Variance-Reduced Cubic Regularized Newton Method.
CoRR
Dongruo Zhou, Pan Xu, Quanquan Gu
2018 A* conf
Stochastic Variance-Reduced Hamilton Monte Carlo Methods.
ICML
Difan Zou, Pan Xu, Quanquan Gu
2018 J jnl
Stochastic Variance-Reduced Hamilton Monte Carlo Methods.
CoRR
Difan Zou, Pan Xu, Quanquan Gu
2018 A conf
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics.
UAI
Difan Zou, Pan Xu, Quanquan Gu
2018 A* conf
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima.
NeurIPS
Yaodong Yu, Pan Xu, Quanquan Gu
2018 A conf
Towards Personalized Learning in Mobile Sensing Systems.
ICDCS
Wenjun Jiang, Qi Li, Lu Su, Chenglin Miao, Quanquan Gu, Wenyao Xu
2017 A conf
A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation.
AISTATS
Lingxiao Wang, Xiao Zhang, Quanquan Gu
2017 A* conf
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery.
ICML
Lingxiao Wang, Xiao Zhang, Quanquan Gu
2017 A conf
Communication-efficient Distributed Sparse Linear Discriminant Analysis.
AISTATS
Lu Tian, Quanquan Gu
2017 A conf
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent.
AISTATS
Pan Xu, Tingting Zhang, Quanquan Gu
2017 A* conf
Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization.
KDD
Jinghui Chen, Quanquan Gu
2017 J jnl
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization.
CoRR
Pan Xu, Jinghui Chen, Quanquan Gu
2017 A* conf
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm.
ICML
Rongda Zhu, Lingxiao Wang, ChengXiang Zhai, Quanquan Gu
2017 A conf
High-dimensional Time Series Clustering via Cross-Predictability.
AISTATS
Dezhi Hong, Quanquan Gu, Kamin Whitehouse
2017 A* conf
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption.
ICML
Lingxiao Wang, Quanquan Gu
2017 J jnl
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption.
CoRR
Jinghui Chen, Lingxiao Wang, Xiao Zhang, Quanquan Gu
2017 J jnl
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently.
CoRR
Yaodong Yu, Difan Zou, Quanquan Gu
2017 — conf
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization.
NIPS
Pan Xu, Jian Ma, Quanquan Gu
2017 J jnl
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations.
CoRR
Pan Xu, Jian Ma, Quanquan Gu
2017 J jnl
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima.
CoRR
Yaodong Yu, Pan Xu, Quanquan Gu
2017 A* conf
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference.
ICML
Aditya Chaudhry, Pan Xu, Quanquan Gu
2016 A* conf
Accelerated Stochastic Block Coordinate Descent with Optimal Sampling.
KDD
Aston Zhang, Quanquan Gu
2016 A conf
Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization.
UAI
Jinghui Chen, Quanquan Gu
2016 A* conf
Contextual Bandits in a Collaborative Environment.
SIGIR
Qingyun Wu, Huazheng Wang, Quanquan Gu, Hongning Wang
2016 A conf
Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates.
UAI
Lu Tian, Pan Xu, Quanquan Gu
2016 A conf
Low-Rank and Sparse Structure Pursuit via Alternating Minimization.
AISTATS
Quanquan Gu, Zhaoran Wang, Han Liu
2016 A* conf
On the Statistical Limits of Convex Relaxations.
ICML
Zhaoran Wang, Quanquan Gu, Han Liu
2016 A conf
Optimal Statistical and Computational Rates for One Bit Matrix Completion.
AISTATS
Renkun Ni, Quanquan Gu
2016 A conf
Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates.
AISTATS
Lingxiao Wang, Xiang Ren, Quanquan Gu
2016 — conf
Semiparametric Differential Graph Models.
NIPS
Pan Xu, Quanquan Gu
2016 A* conf
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation.
ICML
Huan Gui, Jiawei Han, Quanquan Gu
2015 J jnl
A Framework of Mining Trajectories from Untrustworthy Data in Cyber-Physical System.
ACM Trans. Knowl. Discov. Data
Lu-An Tang, Xiao Yu, Quanquan Gu, Jiawei Han, Guofei Jiang, Alice Leung, Thomas La Porta
2015 A conf
Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks.
CIKM
Chang Wan, Xiang Li, Ben Kao, Xiao Yu, Quanquan Gu, David Wai-Lok Cheung, Jiawei Han
2015 A conf
GIN: A Clustering Model for Capturing Dual Heterogeneity in Networked Data.
SDM
Jialu Liu, Chi Wang, Jing Gao, Quanquan Gu, Charu C. Aggarwal, Lance M. Kaplan, Jiawei Han
2015 — conf
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality.
NIPS
Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu
2015 — conf
Robust Classification of Information Networks by Consistent Graph Learning.
ECML/PKDD (2)
Shi Zhi, Jiawei Han, Quanquan Gu
2015 A* conf
Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing.
ICML
Rongda Zhu, Quanquan Gu
2014 J jnl
A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression.
BMC Bioinform.
Yiyi Liu, Quanquan Gu, Jack P. Hou, Jiawei Han, Jian Ma
2014 — ch.
Active Learning: A Survey.
Data Classification: Algorithms and Applications
Charu C. Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, Philip S. Yu
2014 A conf
Batch-Mode Active Learning via Error Bound Minimization.
UAI
Quanquan Gu, Tong Zhang, Jiawei Han
2014 A* conf
ClusCite: effective citation recommendation by information network-based clustering.
KDD
Xiang Ren, Jialu Liu, Xiao Yu, Urvashi Khandelwal, Quanquan Gu, Lidan Wang, Jiawei Han
2014 A* conf
Online Spectral Learning on a Graph with Bandit Feedback.
ICDM
Quanquan Gu, Jiawei Han
2014 — —
Online and active learning of big networks: theory and algorithms
Quanquan Gu
2014 A conf
Personalized entity recommendation: a heterogeneous information network approach.
WSDM
Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, Jiawei Han
2014 — conf
Robust Tensor Decomposition with Gross Corruption.
NIPS
Quanquan Gu, Huan Gui, Jiawei Han
2014 — conf
Sparse PCA with Oracle Property.
NIPS
Quanquan Gu, Zhaoran Wang, Han Liu
2013 A conf
Clustered Support Vector Machines.
AISTATS
Quanquan Gu, Jiawei Han
2013 A* conf
Mining lines in the sand: on trajectory discovery from untrustworthy data in cyber-physical system.
KDD
Lu-An Tang, Xiao Yu, Quanquan Gu, Jiawei Han, Alice Leung, Thomas La Porta
2013 A conf
Recommendation in heterogeneous information networks with implicit user feedback.
RecSys
Xiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, Jiawei Han
2013 A* conf
Selective sampling on graphs for classification.
KDD
Quanquan Gu, Charu C. Aggarwal, Jialu Liu, Jiawei Han
2013 J jnl
Trustworthiness analysis of sensor data in cyber-physical systems.
J. Comput. Syst. Sci.
Lu-An Tang, Xiao Yu, Sangkyum Kim, Quanquan Gu, Jiawei Han, Alice Leung, Thomas La Porta
2013 A conf
Unsupervised Link Selection in Networks.
AISTATS
Quanquan Gu, Charu C. Aggarwal, Jiawei Han
2013 C conf
Wireless sensor network data collection by connected cooperative UAVs.
ACC
Peng Wei, Quanquan Gu, Dengfeng Sun
2012 A conf
Citation Prediction in Heterogeneous Bibliographic Networks.
SDM
Xiao Yu, Quanquan Gu, Mianwei Zhou, Jiawei Han
2012 J jnl
Generalized Fisher Score for Feature Selection
CoRR
Quanquan Gu, Zhenhui Li, Jiawei Han
2012 A conf
IntruMine: Mining Intruders in Untrustworthy Data of Cyber-physical Systems.
SDM
Lu-An Tang, Quanquan Gu, Xiao Yu, Jiawei Han, Thomas La Porta, Alice Leung, Tarek F. Abdelzaher, Lance M. Kaplan
2012 J jnl
Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling.
ACM Trans. Intell. Syst. Technol.
Zhijun Yin, Liangliang Cao, Quanquan Gu, Jiawei Han
2012 A conf
Locality Preserving Feature Learning.
AISTATS
Quanquan Gu, Marina Danilevsky, Zhenhui Li, Jiawei Han
2012 — conf
Selective Labeling via Error Bound Minimization.
NIPS
Quanquan Gu, Tong Zhang, Chris H. Q. Ding, Jiawei Han
2012 A* conf
Towards Active Learning on Graphs: An Error Bound Minimization Approach.
ICDM
Quanquan Gu, Jiawei Han
2011 A conf
Correlated multi-label feature selection.
CIKM
Quanquan Gu, Zhenhui Li, Jiawei Han
2011 A conf
Generalized Fisher Score for Feature Selection.
UAI
Quanquan Gu, Zhenhui Li, Jiawei Han
2011 A* conf
Joint Feature Selection and Subspace Learning.
IJCAI
Quanquan Gu, Zhenhui Li, Jiawei Han
2011 A* conf
Learning a Kernel for Multi-Task Clustering.
AAAI
Quanquan Gu, Zhenhui Li, Jiawei Han
2011 — conf
Linear Discriminant Dimensionality Reduction.
ECML/PKDD (1)
Quanquan Gu, Zhenhui Li, Jiawei Han
2011 A* conf
On Trivial Solution and Scale Transfer Problems in Graph Regularized NMF.
IJCAI
Quanquan Gu, Chris H. Q. Ding, Jiawei Han
2011 A conf
Towards feature selection in network.
CIKM
Quanquan Gu, Jiawei Han
2010 A conf
Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs.
SDM
Quanquan Gu, Jie Zhou, Chris H. Q. Ding
2010 B conf
HTF: a novel feature for general crack detection.
ICIP
Han Hu, Quanquan Gu, Jie Zhou
2009 A* conf
Co-clustering on manifolds.
KDD
Quanquan Gu, Jie Zhou
2009 A* conf
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification.
ICDM
Quanquan Gu, Jie Zhou
2009 A* conf
Local Learning Regularized Nonnegative Matrix Factorization.
IJCAI
Quanquan Gu, Jie Zhou
2009 A conf
Local Relevance Weighted Maximum Margin Criterion for Text Classification.
SDM
Quanquan Gu, Jie Zhou
2009 A conf
Multiframe Motion Segmentation via Penalized MAP Estimation and Linear Programming.
BMVC
Han Hu, Quanquan Gu, Lei Deng, Jie Zhou
2009 B conf
Multiple Kernel Maximum Margin Criterion.
ICIP
Quanquan Gu, Jie Zhou
2009 A conf
Neighborhood Preserving Nonnegative Matrix Factorization.
BMVC
Quanquan Gu, Jie Zhou
2009 Misc conf
Regular simplex criterion: A novel feature extraction criterion.
ICASSP
Quanquan Gu, Jie Zhou
2009 A conf
Subspace maximum margin clustering.
CIKM
Quanquan Gu, Jie Zhou
2009 — conf
Transductive Classification via Dual Regularization.
ECML/PKDD (1)
Quanquan Gu, Jie Zhou
2009 B conf
Two Dimensional Nonnegative Matrix Factorization.
ICIP
Quanquan Gu, Jie Zhou
2009 Misc conf
Two dimensional Maximum Margin Criterion.
ICASSP
Quanquan Gu, Jie Zhou
2008 Misc conf
A novel similarity measure under Riemannian metric for stereo matching.
ICASSP
Quanquan Gu, Jie Zhou
2008 B conf
A similarity measure under Log-Euclidean metric for stereo matching.
ICPR
Quanquan Gu, Jie Zhou
2008 B conf
Belief propagation on Riemannian manifold for stereo matching.
ICIP
Quanquan Gu, Jie Zhou