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2020 – today
- 2024
[j12]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 Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan 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:
Challenges of COVID-19 Case Forecasting in the US, 2020-2021. PLoS Comput. Biol. 20(5): 1011200 (2024)- [j11]Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu:
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. Trans. Mach. Learn. Res. 2024 (2024) - [c215]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:
Large Language Models Can Be Contextual Privacy Protection Learners. EMNLP 2024: 14179-14201 - [c214]Zixiang Chen, Yihe Deng, Yuanzhi Li, Quanquan Gu:
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP. ICLR 2024 - [c213]Qiwei Di, Tao Jin, Yue Wu, Heyang Zhao, Farzad Farnoud, Quanquan Gu:
Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits. ICLR 2024 - [c212]Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu:
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning. ICLR 2024 - [c211]Kaixuan Ji, Qingyue Zhao, Jiafan He, Weitong Zhang, Quanquan Gu:
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. ICLR 2024 - [c210]Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu:
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. ICLR 2024 - [c209]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ICLR 2024 - [c208]Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, Quanquan Gu:
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization. ICLR 2024 - [c207]Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu:
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models. ICML 2024 - [c206]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:
Position: TrustLLM: Trustworthiness in Large Language Models. ICML 2024 - [c205]Xuheng Li, Heyang Zhao, Quanquan Gu:
Feel-Good Thompson Sampling for Contextual Dueling Bandits. ICML 2024 - [c204]Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, Quanquan Gu:
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models. ICML 2024 - [c203]Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu:
Diffusion Language Models Are Versatile Protein Learners. ICML 2024 - [c202]Yue Wu, Tao Jin, Qiwei Di, Hao Lou, Farzad Farnoud, Quanquan Gu:
Borda Regret Minimization for Generalized Linear Dueling Bandits. ICML 2024 - [c201]Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang:
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. ICML 2024 - [c200]Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Uncertainty-Aware Reward-Free Exploration with General Function Approximation. ICML 2024 - [c199]Zijie Huang, Jeehyun Hwang, Junkai Zhang, Jinwoo Baik, Weitong Zhang, Dominik Wodarz, Yizhou Sun, Quanquan Gu, Wei Wang:
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems. WWW 2024: 4607-4617 - [i163]Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu:
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models. CoRR abs/2401.01335 (2024) - [i162]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:
TrustLLM: Trustworthiness in Large Language Models. CoRR abs/2401.05561 (2024) - [i161]Linxi Zhao, Yihe Deng, Weitong Zhang, Quanquan Gu:
Mitigating Object Hallucination in Large Vision-Language Models via Classifier-Free Guidance. CoRR abs/2402.08680 (2024) - [i160]Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang:
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. CoRR abs/2402.08991 (2024) - [i159]Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. CoRR abs/2402.08998 (2024) - [i158]Kaixuan Ji, Jiafan He, Quanquan Gu:
Reinforcement Learning from Human Feedback with Active Queries. CoRR abs/2402.09401 (2024) - [i157]Huizhuo Yuan, Zixiang Chen, Kaixuan Ji, Quanquan Gu:
Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation. CoRR abs/2402.10210 (2024) - [i156]Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu:
Diffusion Language Models Are Versatile Protein Learners. CoRR abs/2402.18567 (2024) - [i155]Zijie Huang, Jeehyun Hwang, Junkai Zhang, Jinwoo Baik, Weitong Zhang, Dominik Wodarz, Yizhou Sun, Quanquan Gu, Wei Wang:
Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems. CoRR abs/2403.00178 (2024) - [i154]Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu:
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. CoRR abs/2403.07902 (2024) - [i153]Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, Quanquan Gu:
DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization. CoRR abs/2403.13829 (2024) - [i152]Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, Quanquan Gu:
Protein Conformation Generation via Force-Guided SE(3) Diffusion Models. CoRR abs/2403.14088 (2024) - [i151]Xiangxin Zhou, Dongyu Xue, Ruizhe Chen, Zaixiang Zheng, Liang Wang, Quanquan Gu:
Antigen-Specific Antibody Design via Direct Energy-based Preference Optimization. CoRR abs/2403.16576 (2024) - [i150]Xuheng Li, Heyang Zhao, Quanquan Gu:
Feel-Good Thompson Sampling for Contextual Dueling Bandits. CoRR abs/2404.06013 (2024) - [i149]Weitong Zhang, Zhiyuan Fan, Jiafan He, Quanquan Gu:
Settling Constant Regrets in Linear Markov Decision Processes. CoRR abs/2404.10745 (2024) - [i148]Qiwei Di, Jiafan He, Quanquan Gu:
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback. CoRR abs/2404.10776 (2024) - [i147]Zixiang Chen, Jun Han, Yongqian Li, Yiwen Kou, Eran Halperin, Robert E. Tillman, Quanquan Gu:
Guided Discrete Diffusion for Electronic Health Record Generation. CoRR abs/2404.12314 (2024) - [i146]Yiwen Kou, Zixiang Chen, Quanquan Gu, Sham M. Kakade:
Matching the Statistical Query Lower Bound for k-sparse Parity Problems with Stochastic Gradient Descent. CoRR abs/2404.12376 (2024) - [i145]Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, Quanquan Gu:
Self-Play Preference Optimization for Language Model Alignment. CoRR abs/2405.00675 (2024) - [i144]Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Uncertainty-Aware Reward-Free Exploration with General Function Approximation. CoRR abs/2406.16255 (2024) - [i143]Xiwei Cheng, Xiangxin Zhou, Yuwei Yang, Yu Bao, Quanquan Gu:
Decomposed Direct Preference Optimization for Structure-Based Drug Design. CoRR abs/2407.13981 (2024) - [i142]Binshuai Wang, Qiwei Di, Ming Yin, Mengdi Wang, Quanquan Gu, Peng Wei:
Relative-Translation Invariant Wasserstein Distance. CoRR abs/2409.02416 (2024) - [i141]Fei Ye, Zaixiang Zheng, Dongyu Xue, Yuning Shen, Lihao Wang, Yiming Ma, Yan Wang, Xinyou Wang, Xiangxin Zhou, Quanquan Gu:
ProteinBench: A Holistic Evaluation of Protein Foundation Models. CoRR abs/2409.06744 (2024) - [i140]Yifan Zhang, Ge Zhang, Yue Wu, Kangping Xu, Quanquan Gu:
General Preference Modeling with Preference Representations for Aligning Language Models. CoRR abs/2410.02197 (2024) - [i139]Zikun Zhang, Zixiang Chen, Quanquan Gu:
Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis. CoRR abs/2410.02321 (2024) - [i138]Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, Chunyuan Li:
LLaVA-Critic: Learning to Evaluate Multimodal Models. CoRR abs/2410.02712 (2024) - [i137]Jiafan He, Huizhuo Yuan, Quanquan Gu:
Accelerated Preference Optimization for Large Language Model Alignment. CoRR abs/2410.06293 (2024) - [i136]Yi Zhou, Yilai Li, Jing Yuan, Quanquan Gu:
CryoFM: A Flow-based Foundation Model for Cryo-EM Densities. CoRR abs/2410.08631 (2024) - [i135]Guanlin Liu, Kaixuan Ji, Renjie Zheng, Zheng Wu, Chen Dun, Quanquan Gu, Lin Yan:
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization. CoRR abs/2410.09302 (2024) - [i134]Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu:
DPLM-2: A Multimodal Diffusion Protein Language Model. CoRR abs/2410.13782 (2024) - [i133]Runjia Li, Qiwei Di, Quanquan Gu:
Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers. CoRR abs/2410.14237 (2024) - [i132]Chen Yang, Chenyang Zhao, Quanquan Gu, Dongruo Zhou:
CoPS: Empowering LLM Agents with Provable Cross-Task Experience Sharing. CoRR abs/2410.16670 (2024) - 2023
- [j10]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. J. Mach. Learn. Res. 24: 326:1-326:58 (2023) - [c198]Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. COLT 2023: 4977-5020 - [c197]Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu:
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. COLT 2023: 5699-5753 - [c196]Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. ICLR 2023 - [c195]Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu:
How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. ICLR 2023 - [c194]Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. ICLR 2023 - [c193]Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu:
Understanding Train-Validation Split in Meta-Learning with Neural Networks. ICLR 2023 - [c192]Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. ICML 2023: 7837-7864 - [c191]Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu:
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. ICML 2023: 11827-11846 - [c190]Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. ICML 2023: 12790-12822 - [c189]Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu:
Benign Overfitting in Two-layer ReLU Convolutional Neural Networks. ICML 2023: 17615-17659 - [c188]Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan:
Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. ICML 2023: 20351-20383 - [c187]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. ICML 2023: 24785-24811 - [c186]Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng:
Personalized Federated Learning under Mixture of Distributions. ICML 2023: 37860-37879 - [c185]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. ICML 2023: 37919-37951 - [c184]Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang:
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. ICML 2023: 39834-39863 - [c183]Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu:
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. ICML 2023: 41111-41132 - [c182]Junkai Zhang, Weitong Zhang, Quanquan Gu:
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs. ICML 2023: 41902-41930 - [c181]Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu:
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. ICML 2023: 42259-42279 - [c180]Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu:
Structure-informed Language Models Are Protein Designers. ICML 2023: 42317-42338 - [c179]Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
The Benefits of Mixup for Feature Learning. ICML 2023: 43423-43479 - [c178]Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu:
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? NeurIPS 2023 - [c177]Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu:
Robust Learning with Progressive Data Expansion Against Spurious Correlation. NeurIPS 2023 - [c176]Yiwen Kou, Zixiang Chen, Quanquan Gu:
Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data. NeurIPS 2023 - [c175]Chenlu Ye, Rui Yang, Quanquan Gu, Tong Zhang:
Corruption-Robust Offline Reinforcement Learning with General Function Approximation. NeurIPS 2023 - [c174]Angela Yuan, Chris Junchi Li, Gauthier Gidel, Michael I. Jordan, Quanquan Gu, Simon S. Du:
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure. NeurIPS 2023 - [c173]Jinghui Chen, Yuan Cao, Quanquan Gu:
Benign Overfitting in Adversarially Robust Linear Classification. UAI 2023: 313-323 - [c172]Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu:
Efficient Privacy-Preserving Stochastic Nonconvex Optimization. UAI 2023: 2203-2213 - [c171]Yue Wu, Jiafan He, Quanquan Gu:
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. UAI 2023: 2304-2313 - [c170]Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu:
Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. UAI 2023: 2488-2497 - [i131]Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu:
Structure-informed Language Models Are Protein Designers. CoRR abs/2302.01649 (2023) - [i130]Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. CoRR abs/2302.10371 (2023) - [i129]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Learning High-Dimensional Single-Neuron ReLU Networks with Finite Samples. CoRR abs/2303.02255 (2023) - [i128]Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu:
Benign Overfitting for Two-layer ReLU Networks. CoRR abs/2303.04145 (2023) - [i127]Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
The Benefits of Mixup for Feature Learning. CoRR abs/2303.08433 (2023) - [i126]Yue Wu, Tao Jin, Hao Lou, Farzad Farnoud, Quanquan Gu:
Borda Regret Minimization for Generalized Linear Dueling Bandits. CoRR abs/2303.08816 (2023) - [i125]Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu:
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. CoRR abs/2303.09390 (2023) - [i124]Junkai Zhang, Weitong Zhang, Quanquan Gu:
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs. CoRR abs/2303.10165 (2023) - [i123]Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng:
Personalized Federated Learning under Mixture of Distributions. CoRR abs/2305.01068 (2023) - [i122]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. CoRR abs/2305.06446 (2023) - [i121]Yue Wu, Jiafan He, Quanquan Gu:
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. CoRR abs/2305.08350 (2023) - [i120]Kaixuan Ji, Qingyue Zhao, Jiafan He, Weitong Zhang, Quanquan Gu:
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. CoRR abs/2305.08359 (2023) - [i119]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:
Beyond One-Model-Fits-All: A Survey of Domain Specialization for Large Language Models. CoRR abs/2305.18703 (2023) - [i118]Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu:
Robust Learning with Progressive Data Expansion Against Spurious Correlation. CoRR abs/2306.04949 (2023) - [i117]Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu:
The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. CoRR abs/2306.11680 (2023) - [i116]Jiasheng Ye, Zaixiang Zheng, Yu Bao, Lihua Qian, Quanquan Gu:
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning. CoRR abs/2308.12219 (2023) - [i115]Zixiang Chen, Yihe Deng, Yuanzhi Li, Quanquan Gu:
Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP. CoRR abs/2310.00927 (2023) - [i114]Qiwei Di, Tao Jin, Yue Wu, Heyang Zhao, Farzad Farnoud, Quanquan Gu:
Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits. CoRR abs/2310.00968 (2023) - [i113]Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu:
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning. CoRR abs/2310.01380 (2023) - [i112]Zixiang Chen, Junkai Zhang, Yiwen Kou, Xiangning Chen, Cho-Jui Hsieh, Quanquan Gu:
Why Does Sharpness-Aware Minimization Generalize Better Than SGD? CoRR abs/2310.07269 (2023) - [i111]Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett:
How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? CoRR abs/2310.08391 (2023) - [i110]Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu, Huazheng Wang:
Pure Exploration in Asynchronous Federated Bandits. CoRR abs/2310.11015 (2023) - [i109]Chenlu Ye, Rui Yang, Quanquan Gu, Tong Zhang:
Corruption-Robust Offline Reinforcement Learning with General Function Approximation. CoRR abs/2310.14550 (2023) - [i108]Yiwen Kou, Zixiang Chen, Quanquan Gu:
Implicit Bias of Gradient Descent for Two-layer ReLU and Leaky ReLU Networks on Nearly-orthogonal Data. CoRR abs/2310.18935 (2023) - [i107]Yihe Deng, Weitong Zhang, Zixiang Chen, Quanquan Gu:
Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves. CoRR abs/2311.04205 (2023) - [i106]Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu:
Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. CoRR abs/2311.14222 (2023) - [i105]Heyang Zhao, Jiafan He, Quanquan Gu:
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation. CoRR abs/2311.15238 (2023) - [i104]Zixiang Chen, Huizhuo Yuan, Yongqian Li, Yiwen Kou, Junkai Zhang, Quanquan Gu:
Fast Sampling via De-randomization for Discrete Diffusion Models. CoRR abs/2312.09193 (2023) - [i103]Quanquan Gu, Zhaoran Wang, Han Liu:
Sparse PCA with Oracle Property. CoRR abs/2312.16793 (2023) - 2022
- [c169]Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu, Jingjing Liu:
Efficient Robust Training via Backward Smoothing. AAAI 2022: 6222-6230 - [c168]Chonghua Liao, Jiafan He, Quanquan Gu:
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes. ACML 2022: 627-642 - [c167]Yue Wu, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. AISTATS 2022: 3883-3913 - [c166]Jiafan He, Dongruo Zhou, Quanquan Gu:
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. AISTATS 2022: 4259-4280 - [c165]Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu:
Self-training Converts Weak Learners to Strong Learners in Mixture Models. AISTATS 2022: 8003-8021 - [c164]Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu:
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. AISTATS 2022: 11014-11036 - [c163]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. ALT 2022: 176-204 - [c162]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. ALT 2022: 227-261 - [c161]Zhe Wu, Aisha Alnajdi, Quanquan Gu, Panagiotis D. Christofides:
Machine-Learning-based Predictive Control of Nonlinear Processes with Uncertainty. ACC 2022: 2810-2816 - [c160]Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu:
Neural Contextual Bandits with Deep Representation and Shallow Exploration. ICLR 2022 - [c159]Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang:
Learning Neural Contextual Bandits through Perturbed Rewards. ICLR 2022 - [c158]Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh:
On the Convergence of Certified Robust Training with Interval Bound Propagation. ICLR 2022 - [c157]Yuanzhou Chen, Jiafan He, Quanquan Gu:
On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs. ICML 2022: 3149-3183 - [c156]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Learning Stochastic Shortest Path with Linear Function Approximation. ICML 2022: 15584-15629 - [c155]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. ICML 2022: 24280-24314 - [c154]Dongruo Zhou, Quanquan Gu:
Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. ICML 2022: 27143-27158 - [c153]Yuan Cao, Zixiang Chen, Misha Belkin, Quanquan Gu:
Benign Overfitting in Two-layer Convolutional Neural Networks. NeurIPS 2022 - [c152]Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li:
Towards Understanding the Mixture-of-Experts Layer in Deep Learning. NeurIPS 2022 - [c151]Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu:
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. NeurIPS 2022 - [c150]Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. NeurIPS 2022 - [c149]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium. NeurIPS 2022 - [c148]Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud:
Active Ranking without Strong Stochastic Transitivity. NeurIPS 2022 - [c147]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. NeurIPS 2022 - [c146]Dongruo Zhou, Quanquan Gu:
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. NeurIPS 2022 - [c145]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. NeurIPS 2022 - [i102]Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang:
Learning Contextual Bandits Through Perturbed Rewards. CoRR abs/2201.09910 (2022) - [i101]Yuan Cao, Zixiang Chen, Mikhail Belkin, Quanquan Gu:
Benign Overfitting in Two-layer Convolutional Neural Networks. CoRR abs/2202.06526 (2022) - [i100]Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu:
Bandit Learning with General Function Classes: Heteroscedastic Noise and Variance-dependent Regret Bounds. CoRR abs/2202.13603 (2022) - [i99]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime. CoRR abs/2203.03159 (2022) - [i98]Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh:
On the Convergence of Certified Robust Training with Interval Bound Propagation. CoRR abs/2203.08961 (2022) - [i97]Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu:
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions. CoRR abs/2205.06811 (2022) - [i96]Dongruo Zhou, Quanquan Gu:
Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs. CoRR abs/2205.11507 (2022) - [i95]Jiafan He, Tianhao Wang, Yifei Min, Quanquan Gu:
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. CoRR abs/2207.03106 (2022) - [i94]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift. CoRR abs/2208.01857 (2022) - [i93]Zixiang Chen, Yihe Deng, Yue Wu, Quanquan Gu, Yuanzhi Li:
Towards Understanding Mixture of Experts in Deep Learning. CoRR abs/2208.02813 (2022) - [i92]Chris Junchi Li, Dongruo Zhou, Quanquan Gu, Michael I. Jordan:
Learning Two-Player Mixture Markov Games: Kernel Function Approximation and Correlated Equilibrium. CoRR abs/2208.05363 (2022) - [i91]Zixiang Chen, Chris Junchi Li, Angela Yuan, Quanquan Gu, Michael I. Jordan:
A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. CoRR abs/2209.15634 (2022) - [i90]Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang:
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. CoRR abs/2212.05949 (2022) - [i89]Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. CoRR abs/2212.06132 (2022) - 2021
- [j9]Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu, David Evans:
Revisiting Membership Inference Under Realistic Assumptions. Proc. Priv. Enhancing Technol. 2021(2): 348-368 (2021) - [j8]Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher:
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. SIAM J. Sci. Comput. 43(1): A26-A53 (2021) - [c144]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu:
Double Explore-then-Commit: Asymptotic Optimality and Beyond. COLT 2021: 2584-2633 - [c143]Dongruo Zhou, Quanquan Gu, Csaba Szepesvári:
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. COLT 2021: 4532-4576 - [c142]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. COLT 2021: 4633-4635 - [c141]Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu:
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? ICLR 2021 - [c140]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu:
Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. ICLR 2021 - [c139]Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Thompson Sampling. ICLR 2021 - [c138]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. ICML 2021: 3417-3426 - [c137]Spencer Frei, Yuan Cao, Quanquan Gu:
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. ICML 2021: 3427-3438 - [c136]Jiafan He, Dongruo Zhou, Quanquan Gu:
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. ICML 2021: 4171-4180 - [c135]Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu:
Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. ICML 2021: 5065-5073 - [c134]Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu:
MOTS: Minimax Optimal Thompson Sampling. ICML 2021: 5074-5083 - [c133]Dongruo Zhou, Jiafan He, Quanquan Gu:
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. ICML 2021: 12793-12802 - [c132]Difan Zou, Spencer Frei, Quanquan Gu:
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. ICML 2021: 13002-13011 - [c131]Difan Zou, Quanquan Gu:
On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. ICML 2021: 13012-13022 - [c130]Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu:
Towards Understanding the Spectral Bias of Deep Learning. IJCAI 2021: 2205-2211 - [c129]Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang:
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks. NAACL-HLT 2021: 2609-2615 - [c128]Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. NeurIPS 2021: 1582-1593 - [c127]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade:
The Benefits of Implicit Regularization from SGD in Least Squares Problems. NeurIPS 2021: 5456-5468 - [c126]Hanxun Huang, Yisen Wang, Sarah M. Erfani, Quanquan Gu, James Bailey, Xingjun Ma:
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. NeurIPS 2021: 5545-5559 - [c125]Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu:
Do Wider Neural Networks Really Help Adversarial Robustness? NeurIPS 2021: 7054-7067 - [c124]Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Variance-Aware Off-Policy Evaluation with Linear Function Approximation. NeurIPS 2021: 7598-7610 - [c123]Spencer Frei, Quanquan Gu:
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent. NeurIPS 2021: 7937-7949 - [c122]Yuan Cao, Quanquan Gu, Mikhail Belkin:
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. NeurIPS 2021: 8407-8418 - [c121]Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert Nowak:
Pure Exploration in Kernel and Neural Bandits. NeurIPS 2021: 11618-11630 - [c120]Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints. NeurIPS 2021: 13524-13536 - [c119]Jiafan He, Dongruo Zhou, Quanquan Gu:
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. NeurIPS 2021: 14188-14199 - [c118]Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs. NeurIPS 2021: 22288-22300 - [c117]Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu:
Iterative Teacher-Aware Learning. NeurIPS 2021: 29231-29245 - [c116]Difan Zou, Pan Xu, Quanquan Gu:
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. UAI 2021: 1152-1162 - [i88]Spencer Frei, Yuan Cao, Quanquan Gu:
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. CoRR abs/2101.01152 (2021) - [i87]Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Provably Efficient Reinforcement Learning with Linear Function Approximation Under Adaptivity Constraints. CoRR abs/2101.02195 (2021) - [i86]Yue Wu, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. CoRR abs/2102.07301 (2021) - [i85]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Almost Optimal Algorithms for Two-player Markov Games with Linear Function Approximation. CoRR abs/2102.07404 (2021) - [i84]Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Optimal Regret for Learning Adversarial MDPs with Linear Function Approximation. CoRR abs/2102.08940 (2021) - [i83]Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab S. Mirrokni, Dongruo Zhou:
Batched Neural Bandits. CoRR abs/2102.13028 (2021) - [i82]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Benign Overfitting of Constant-Stepsize SGD for Linear Regression. CoRR abs/2103.12692 (2021) - [i81]Difan Zou, Spencer Frei, Quanquan Gu:
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. CoRR abs/2104.09437 (2021) - [i80]Yuan Cao, Quanquan Gu, Mikhail Belkin:
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures. CoRR abs/2104.13628 (2021) - [i79]Jiafan He, Dongruo Zhou, Quanquan Gu:
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation. CoRR abs/2106.11612 (2021) - [i78]Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu:
Provably Efficient Representation Learning in Low-rank Markov Decision Processes. CoRR abs/2106.11935 (2021) - [i77]Yifei Min, Tianhao Wang, Dongruo Zhou, Quanquan Gu:
Variance-Aware Off-Policy Evaluation with Linear Function Approximation. CoRR abs/2106.11960 (2021) - [i76]Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang, Quanquan Gu, Rebecca Willett, Robert D. Nowak:
Pure Exploration in Kernel and Neural Bandits. CoRR abs/2106.12034 (2021) - [i75]Spencer Frei, Quanquan Gu:
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent. CoRR abs/2106.13792 (2021) - [i74]Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu:
Self-training Converts Weak Learners to Strong Learners in Mixture Models. CoRR abs/2106.13805 (2021) - [i73]Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade:
The Benefits of Implicit Regularization from SGD in Least Squares Problems. CoRR abs/2108.04552 (2021) - [i72]Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu:
Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. CoRR abs/2108.11371 (2021) - [i71]Luyao Yuan, Dongruo Zhou, Junhong Shen, Jingdong Gao, Jeffrey L. Chen, Quanquan Gu, Ying Nian Wu, Song-Chun Zhu:
Iterative Teacher-Aware Learning. CoRR abs/2110.00137 (2021) - [i70]Hanxun Huang, Yisen Wang, Sarah Monazam Erfani, Quanquan Gu, James Bailey, Xingjun Ma:
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks. CoRR abs/2110.03825 (2021) - [i69]Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu:
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. CoRR abs/2110.04136 (2021) - [i68]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade:
Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. CoRR abs/2110.06198 (2021) - [i67]Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation. CoRR abs/2110.06394 (2021) - [i66]Xiaoxia Wu, Lingxiao Wang, Irina Cristali, Quanquan Gu, Rebecca Willett:
Adaptive Differentially Private Empirical Risk Minimization. CoRR abs/2110.07435 (2021) - [i65]Chonghua Liao, Jiafan He, Quanquan Gu:
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes. CoRR abs/2110.10133 (2021) - [i64]Heyang Zhao, Dongruo Zhou, Quanquan Gu:
Linear Contextual Bandits with Adversarial Corruptions. CoRR abs/2110.12615 (2021) - [i63]Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu:
Learning Stochastic Shortest Path with Linear Function Approximation. CoRR abs/2110.12727 (2021) - [i62]Zixiang Chen, Dongruo Zhou, Quanquan Gu:
Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. CoRR abs/2110.13144 (2021) - [i61]Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu:
On the Convergence and Robustness of Adversarial Training. CoRR abs/2112.08304 (2021) - [i60]Jinghui Chen, Yuan Cao, Quanquan Gu:
Benign Overfitting in Adversarially Robust Linear Classification. CoRR abs/2112.15250 (2021) - 2020
- [j7]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduction for Nonconvex Optimization. J. Mach. Learn. Res. 21: 103:1-103:63 (2020) - [j6]Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu:
Gradient descent optimizes over-parameterized deep ReLU networks. Mach. Learn. 109(3): 467-492 (2020) - [c115]Yuan Cao, Quanquan Gu:
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks. AAAI 2020: 3349-3356 - [c114]Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu:
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. AAAI 2020: 3486-3494 - [c113]Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud:
Rank Aggregation via Heterogeneous Thurstone Preference Models. AAAI 2020: 4353-4360 - [c112]Lingxiao Wang, Quanquan Gu:
A Knowledge Transfer Framework for Differentially Private Sparse Learning. AAAI 2020: 6235-6242 - [c111]Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans:
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models. AISTATS 2020: 3883-3893 - [c110]Dongruo Zhou, Quanquan Gu:
Stochastic Recursive Variance-Reduced Cubic Regularization Methods. AISTATS 2020: 3980-3990 - [c109]Dongruo Zhou, Yuan Cao, Quanquan Gu:
Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. AISTATS 2020: 4430-4440 - [c108]Pan Xu, Felicia Gao, Quanquan Gu:
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. ICLR 2020 - [c107]Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu:
Improving Adversarial Robustness Requires Revisiting Misclassified Examples. ICLR 2020 - [c106]Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu:
Improving Neural Language Generation with Spectrum Control. ICLR 2020 - [c105]Difan Zou, Philip M. Long, Quanquan Gu:
On the Global Convergence of Training Deep Linear ResNets. ICLR 2020 - [c104]Yonatan Dukler, Quanquan Gu, Guido Montúfar:
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. ICML 2020: 2751-2760 - [c103]Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. ICML 2020: 10555-10565 - [c102]Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Contextual Bandits with UCB-based Exploration. ICML 2020: 11492-11502 - [c101]Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu:
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. IJCAI 2020: 3267-3275 - [c100]Jinghui Chen, Quanquan Gu:
RayS: A Ray Searching Method for Hard-label Adversarial Attack. KDD 2020: 1739-1747 - [c99]Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat, Stanley J. Osher:
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. MSML 2020: 328-351 - [c98]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks. NeurIPS 2020 - [c97]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of a Single Neuron with Gradient Descent. NeurIPS 2020 - [c96]Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods. NeurIPS 2020 - [c95]Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu, Miryung Kim:
Is neuron coverage a meaningful measure for testing deep neural networks? ESEC/SIGSOFT FSE 2020: 851-862 - [i59]Zixiang Chen, Yuan Cao, Quanquan Gu, Tong Zhang:
Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds. CoRR abs/2002.04026 (2020) - [i58]Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu:
Double Explore-then-Commit: Asymptotic Optimality and Beyond. CoRR abs/2002.09174 (2020) - [i57]Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans:
Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models. CoRR abs/2003.00378 (2020) - [i56]Difan Zou, Philip M. Long, Quanquan Gu:
On the Global Convergence of Training Deep Linear ResNets. CoRR abs/2003.01094 (2020) - [i55]Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu:
MOTS: Minimax Optimal Thompson Sampling. CoRR abs/2003.01803 (2020) - [i54]Zhicong Liang, Bao Wang, Quanquan Gu, Stanley J. Osher, Yuan Yao:
Exploring Private Federated Learning with Laplacian Smoothing. CoRR abs/2005.00218 (2020) - [i53]Yue Wu, Weitong Zhang, Pan Xu, Quanquan Gu:
A Finite Time Analysis of Two Time-Scale Actor Critic Methods. CoRR abs/2005.01350 (2020) - [i52]Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu:
Revisiting Membership Inference Under Realistic Assumptions. CoRR abs/2005.10881 (2020) - [i51]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of a Single Neuron with Gradient Descent. CoRR abs/2005.14426 (2020) - [i50]Yonatan Dukler, Quanquan Gu, Guido Montúfar:
Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. CoRR abs/2006.06878 (2020) - [i49]Jinghui Chen, Quanquan Gu:
RayS: A Ray Searching Method for Hard-label Adversarial Attack. CoRR abs/2006.12792 (2020) - [i48]Dongruo Zhou, Jiafan He, Quanquan Gu:
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. CoRR abs/2006.13165 (2020) - [i47]Spencer Frei, Yuan Cao, Quanquan Gu:
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. CoRR abs/2010.00539 (2020) - [i46]Jiafan He, Dongruo Zhou, Quanquan Gu:
Minimax Optimal Reinforcement Learning for Discounted MDPs. CoRR abs/2010.00587 (2020) - [i45]Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Thompson Sampling. CoRR abs/2010.00827 (2020) - [i44]Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu, Jingjing Liu:
Efficient Robust Training via Backward Smoothing. CoRR abs/2010.01278 (2020) - [i43]Boxi Wu, Jinghui Chen, Deng Cai, Xiaofei He, Quanquan Gu:
Does Network Width Really Help Adversarial Robustness? CoRR abs/2010.01279 (2020) - [i42]Difan Zou, Pan Xu, Quanquan Gu:
Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. CoRR abs/2010.09597 (2020) - [i41]Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu:
Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate. CoRR abs/2011.02538 (2020) - [i40]Dongruo Zhou, Jiahao Chen, Quanquan Gu:
Provable Multi-Objective Reinforcement Learning with Generative Models. CoRR abs/2011.10134 (2020) - [i39]Jiafan He, Dongruo Zhou, Quanquan Gu:
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. CoRR abs/2011.11566 (2020) - [i38]Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu:
Neural Contextual Bandits with Deep Representation and Shallow Exploration. CoRR abs/2012.01780 (2020) - [i37]Dongruo Zhou, Quanquan Gu, Csaba Szepesvári:
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. CoRR abs/2012.08507 (2020)
2010 – 2019
- 2019
- [j5]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularization Methods. J. Mach. Learn. Res. 20: 134:1-134:47 (2019) - [c94]Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu:
Learning One-hidden-layer ReLU Networks via Gradient Descent. AISTATS 2019: 1524-1534 - [c93]Difan Zou, Pan Xu, Quanquan Gu:
Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. AISTATS 2019: 2936-2945 - [c92]Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu:
On the Convergence and Robustness of Adversarial Training. ICML 2019: 6586-6595 - [c91]Dongruo Zhou, Quanquan Gu:
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. ICML 2019: 7574-7583 - [c90]Lingxiao Wang, Quanquan Gu:
Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning. IJCAI 2019: 3740-3747 - [c89]Difan Zou, Quanquan Gu:
An Improved Analysis of Training Over-parameterized Deep Neural Networks. NeurIPS 2019: 2053-2062 - [c88]Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. NeurIPS 2019: 3830-3841 - [c87]Yuan Cao, Quanquan Gu:
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. NeurIPS 2019: 10611-10621 - [c86]Yuan Cao, Quanquan Gu:
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. NeurIPS 2019: 10835-10845 - [c85]Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu:
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. NeurIPS 2019: 11247-11256 - [c84]Spencer Frei, Yuan Cao, Quanquan Gu:
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. NeurIPS 2019: 14769-14779 - [c83]Pan Xu, Felicia Gao, Quanquan Gu:
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. UAI 2019: 541-551 - [i36]Dongruo Zhou, Quanquan Gu:
Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. CoRR abs/1901.11224 (2019) - [i35]Dongruo Zhou, Quanquan Gu:
Stochastic Recursive Variance-Reduced Cubic Regularization Methods. CoRR abs/1901.11518 (2019) - [i34]Yuan Cao, Quanquan Gu:
A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks. CoRR abs/1902.01384 (2019) - [i33]Pan Xu, Felicia Gao, Quanquan Gu:
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. CoRR abs/1905.12615 (2019) - [i32]Yuan Cao, Quanquan Gu:
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks. CoRR abs/1905.13210 (2019) - [i31]Difan Zou, Quanquan Gu:
An Improved Analysis of Training Over-parameterized Deep Neural Networks. CoRR abs/1906.04688 (2019) - [i30]Bao Wang, Quanquan Gu, March Boedihardjo, Farzin Barekat, Stanley J. Osher:
DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM. CoRR abs/1906.12056 (2019) - [i29]Lingxiao Wang, Quanquan Gu:
A Knowledge Transfer Framework for Differentially Private Sparse Learning. CoRR abs/1909.06322 (2019) - [i28]Pan Xu, Felicia Gao, Quanquan Gu:
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. CoRR abs/1909.08610 (2019) - [i27]Spencer Frei, Yuan Cao, Quanquan Gu:
Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. CoRR abs/1910.02934 (2019) - [i26]Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu:
Efficient Privacy-Preserving Nonconvex Optimization. CoRR abs/1910.13659 (2019) - [i25]Bao Wang, Difan Zou, Quanquan Gu, Stanley J. Osher:
Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo. CoRR abs/1911.00782 (2019) - [i24]Dongruo Zhou, Lihong Li, Quanquan Gu:
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration. CoRR abs/1911.04462 (2019) - [i23]Yuan Cao, Quanquan Gu:
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks. CoRR abs/1911.05059 (2019) - [i22]Difan Zou, Ziniu Hu, Yewen Wang, Song Jiang, Yizhou Sun, Quanquan Gu:
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. CoRR abs/1911.07323 (2019) - [i21]Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu:
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? CoRR abs/1911.12360 (2019) - [i20]Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu:
Towards Understanding the Spectral Bias of Deep Learning. CoRR abs/1912.01198 (2019) - [i19]Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud:
Rank Aggregation via Heterogeneous Thurstone Preference Models. CoRR abs/1912.01211 (2019) - [i18]Pan Xu, Quanquan Gu:
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. CoRR abs/1912.04511 (2019) - 2018
- [c82]Pan Xu, Tianhao Wang, Quanquan Gu:
Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. AISTATS 2018: 1087-1096 - [c81]Xiao Zhang, Lingxiao Wang, Quanquan Gu:
A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. AISTATS 2018: 1097-1107 - [c80]Wenjun Jiang, Qi Li, Lu Su, Chenglin Miao, Quanquan Gu, Wenyao Xu:
Towards Personalized Learning in Mobile Sensing Systems. ICDCS 2018: 321-333 - [c79]Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu:
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. ICML 2018: 921-930 - [c78]Pan Xu, Tianhao Wang, Quanquan Gu:
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions. ICML 2018: 5488-5497 - [c77]Xiao Zhang, Simon S. Du, Quanquan Gu:
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow. ICML 2018: 5751-5760 - [c76]Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu:
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. ICML 2018: 5857-5866 - [c75]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularized Newton Method. ICML 2018: 5985-5994 - [c74]Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. ICML 2018: 6023-6032 - [c73]Pan Xu, Jinghui Chen, Difan Zou, Quanquan Gu:
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. NeurIPS 2018: 3126-3137 - [c72]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization. NeurIPS 2018: 3925-3936 - [c71]Yaodong Yu, Pan Xu, Quanquan Gu:
Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima. NeurIPS 2018: 4530-4540 - [c70]Bargav Jayaraman, Lingxiao Wang, David Evans, Quanquan Gu:
Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization. NeurIPS 2018: 6346-6357 - [c69]Yang Wang, Quanquan Gu, Donald E. Brown:
Differentially Private Hypothesis Transfer Learning. ECML/PKDD (2) 2018: 811-826 - [c68]Yang Yang, Quanquan Gu, Takayo Sasaki, Julianna Crivello, Rachel O'Neill, David M. Gilbert, Jian Ma:
Continuous-Trait Probabilistic Model for Comparing Multi-species Functional Genomic Data. RECOMB 2018: 293-294 - [c67]Difan Zou, Pan Xu, Quanquan Gu:
Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. UAI 2018: 508-518 - [i17]Difan Zou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Hamilton Monte Carlo Methods. CoRR abs/1802.04791 (2018) - [i16]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Variance-Reduced Cubic Regularized Newton Method. CoRR abs/1802.04796 (2018) - [i15]Xiao Zhang, Simon S. Du, Quanquan Gu:
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow. CoRR abs/1803.01233 (2018) - [i14]Jinghui Chen, Quanquan Gu:
Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. CoRR abs/1806.06763 (2018) - [i13]Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu:
Learning One-hidden-layer ReLU Networks via Gradient Descent. CoRR abs/1806.07808 (2018) - [i12]Dongruo Zhou, Pan Xu, Quanquan Gu:
Stochastic Nested Variance Reduction for Nonconvex Optimization. CoRR abs/1806.07811 (2018) - [i11]Dongruo Zhou, Pan Xu, Quanquan Gu:
Finding Local Minima via Stochastic Nested Variance Reduction. CoRR abs/1806.08782 (2018) - [i10]Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu:
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. CoRR abs/1808.05671 (2018) - [i9]Difan Zou, Yuan Cao, Dongruo Zhou, Quanquan Gu:
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks. CoRR abs/1811.08888 (2018) - [i8]Jinghui Chen, Jinfeng Yi, Quanquan Gu:
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. CoRR abs/1811.10828 (2018) - [i7]Dongruo Zhou, Pan Xu, Quanquan Gu:
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method. CoRR abs/1811.11989 (2018) - 2017
- [c66]Dezhi Hong, Quanquan Gu, Kamin Whitehouse:
High-dimensional Time Series Clustering via Cross-Predictability. AISTATS 2017: 642-651 - [c65]Pan Xu, Tingting Zhang, Quanquan Gu:
Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent. AISTATS 2017: 923-932 - [c64]Lingxiao Wang, Xiao Zhang, Quanquan Gu:
A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation. AISTATS 2017: 981-990 - [c63]Lu Tian, Quanquan Gu:
Communication-efficient Distributed Sparse Linear Discriminant Analysis. AISTATS 2017: 1178-1187 - [c62]Aditya Chaudhry, Pan Xu, Quanquan Gu:
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. ICML 2017: 684-693 - [c61]Lingxiao Wang, Quanquan Gu:
Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. ICML 2017: 3617-3626 - [c60]Lingxiao Wang, Xiao Zhang, Quanquan Gu:
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. ICML 2017: 3712-3721 - [c59]Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu:
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. ICML 2017: 4180-4188 - [c58]Jinghui Chen, Quanquan Gu:
Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization. KDD 2017: 757-766 - [c57]Pan Xu, Jian Ma, Quanquan Gu:
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization. NIPS 2017: 1933-1944 - [i6]Pan Xu, Jian Ma, Quanquan Gu:
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimizations. CoRR abs/1702.08651 (2017) - [i5]Jinghui Chen, Lingxiao Wang, Xiao Zhang, Quanquan Gu:
Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption. CoRR abs/1704.06256 (2017) - [i4]Pan Xu, Jinghui Chen, Quanquan Gu:
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization. CoRR abs/1707.06618 (2017) - [i3]Yaodong Yu, Difan Zou, Quanquan Gu:
Saving Gradient and Negative Curvature Computations: Finding Local Minima More Efficiently. CoRR abs/1712.03950 (2017) - [i2]Yaodong Yu, Pan Xu, Quanquan Gu:
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima. CoRR abs/1712.06585 (2017) - 2016
- [c56]Lingxiao Wang, Xiang Ren, Quanquan Gu:
Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates. AISTATS 2016: 177-185 - [c55]Renkun Ni, Quanquan Gu:
Optimal Statistical and Computational Rates for One Bit Matrix Completion. AISTATS 2016: 426-434 - [c54]Quanquan Gu, Zhaoran Wang, Han Liu:
Low-Rank and Sparse Structure Pursuit via Alternating Minimization. AISTATS 2016: 600-609 - [c53]Zhaoran Wang, Quanquan Gu, Han Liu:
On the Statistical Limits of Convex Relaxations. ICML 2016: 1368-1377 - [c52]Huan Gui, Jiawei Han, Quanquan Gu:
Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation. ICML 2016: 2300-2309 - [c51]Aston Zhang, Quanquan Gu:
Accelerated Stochastic Block Coordinate Descent with Optimal Sampling. KDD 2016: 2035-2044 - [c50]Pan Xu, Quanquan Gu:
Semiparametric Differential Graph Models. NIPS 2016: 1064-1072 - [c49]Qingyun Wu, Huazheng Wang, Quanquan Gu, Hongning Wang:
Contextual Bandits in a Collaborative Environment. SIGIR 2016: 529-538 - [c48]Jinghui Chen, Quanquan Gu:
Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization. UAI 2016 - [c47]Lu Tian, Pan Xu, Quanquan Gu:
Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates. UAI 2016 - 2015
- [j4]Lu-An Tang, Xiao Yu, Quanquan Gu, Jiawei Han, Guofei Jiang, Alice Leung, Thomas La Porta:
A Framework of Mining Trajectories from Untrustworthy Data in Cyber-Physical System. ACM Trans. Knowl. Discov. Data 9(3): 16:1-16:35 (2015) - [c46]Chang Wan, Xiang Li, Ben Kao, Xiao Yu, Quanquan Gu, David Wai-Lok Cheung, Jiawei Han:
Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks. CIKM 2015: 443-452 - [c45]Rongda Zhu, Quanquan Gu:
Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing. ICML 2015: 739-747 - [c44]Zhaoran Wang, Quanquan Gu, Yang Ning, Han Liu:
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality. NIPS 2015: 2521-2529 - [c43]Shi Zhi, Jiawei Han, Quanquan Gu:
Robust Classification of Information Networks by Consistent Graph Learning. ECML/PKDD (2) 2015: 752-767 - [c42]Jialu Liu, Chi Wang, Jing Gao, Quanquan Gu, Charu C. Aggarwal, Lance M. Kaplan, Jiawei Han:
GIN: A Clustering Model for Capturing Dual Heterogeneity in Networked Data. SDM 2015: 388-396 - 2014
- [b1]Quanquan Gu:
Online and active learning of big networks: theory and algorithms. University of Illinois Urbana-Champaign, USA, 2014 - [j3]Yiyi Liu, Quanquan Gu, Jack P. Hou, Jiawei Han, Jian Ma:
A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression. BMC Bioinform. 15: 37 (2014) - [c41]Quanquan Gu, Jiawei Han:
Online Spectral Learning on a Graph with Bandit Feedback. ICDM 2014: 833-838 - [c40]Xiang Ren, Jialu Liu, Xiao Yu, Urvashi Khandelwal, Quanquan Gu, Lidan Wang, Jiawei Han:
ClusCite: effective citation recommendation by information network-based clustering. KDD 2014: 821-830 - [c39]Quanquan Gu, Huan Gui, Jiawei Han:
Robust Tensor Decomposition with Gross Corruption. NIPS 2014: 1422-1430 - [c38]Quanquan Gu, Zhaoran Wang, Han Liu:
Sparse PCA with Oracle Property. NIPS 2014: 1529-1537 - [c37]Quanquan Gu, Tong Zhang, Jiawei Han:
Batch-Mode Active Learning via Error Bound Minimization. UAI 2014: 300-309 - [c36]Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, Jiawei Han:
Personalized entity recommendation: a heterogeneous information network approach. WSDM 2014: 283-292 - [p1]Charu C. Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, Philip S. Yu:
Active Learning: A Survey. Data Classification: Algorithms and Applications 2014: 571-606 - 2013
- [j2]Lu-An Tang, Xiao Yu, Sangkyum Kim, Quanquan Gu, Jiawei Han, Alice Leung, Thomas La Porta:
Trustworthiness analysis of sensor data in cyber-physical systems. J. Comput. Syst. Sci. 79(3): 383-401 (2013) - [c35]Quanquan Gu, Charu C. Aggarwal, Jiawei Han:
Unsupervised Link Selection in Networks. AISTATS 2013: 298-306 - [c34]Quanquan Gu, Jiawei Han:
Clustered Support Vector Machines. AISTATS 2013: 307-315 - [c33]Peng Wei, Quanquan Gu, Dengfeng Sun:
Wireless sensor network data collection by connected cooperative UAVs. ACC 2013: 5911-5916 - [c32]Quanquan Gu, Charu C. Aggarwal, Jialu Liu, Jiawei Han:
Selective sampling on graphs for classification. KDD 2013: 131-139 - [c31]Lu-An Tang, Xiao Yu, Quanquan Gu, Jiawei Han, Alice Leung, Thomas La Porta:
Mining lines in the sand: on trajectory discovery from untrustworthy data in cyber-physical system. KDD 2013: 410-418 - [c30]Xiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, Jiawei Han:
Recommendation in heterogeneous information networks with implicit user feedback. RecSys 2013: 347-350 - 2012
- [j1]Zhijun Yin, Liangliang Cao, Quanquan Gu, Jiawei Han:
Latent Community Topic Analysis: Integration of Community Discovery with Topic Modeling. ACM Trans. Intell. Syst. Technol. 3(4): 63:1-63:21 (2012) - [c29]Quanquan Gu, Jiawei Han:
Towards Active Learning on Graphs: An Error Bound Minimization Approach. ICDM 2012: 882-887 - [c28]Quanquan Gu, Tong Zhang, Chris H. Q. Ding, Jiawei Han:
Selective Labeling via Error Bound Minimization. NIPS 2012: 332-340 - [c27]Lu-An Tang, Quanquan Gu, Xiao Yu, Jiawei Han, Thomas La Porta, Alice Leung, Tarek F. Abdelzaher, Lance M. Kaplan:
IntruMine: Mining Intruders in Untrustworthy Data of Cyber-physical Systems. SDM 2012: 600-611 - [c26]Xiao Yu, Quanquan Gu, Mianwei Zhou, Jiawei Han:
Citation Prediction in Heterogeneous Bibliographic Networks. SDM 2012: 1119-1130 - [c25]Quanquan Gu, Marina Danilevsky, Zhenhui Li, Jiawei Han:
Locality Preserving Feature Learning. AISTATS 2012: 477-485 - [i1]Quanquan Gu, Zhenhui Li, Jiawei Han:
Generalized Fisher Score for Feature Selection. CoRR abs/1202.3725 (2012) - 2011
- [c24]Quanquan Gu, Zhenhui Li, Jiawei Han:
Learning a Kernel for Multi-Task Clustering. AAAI 2011: 368-373 - [c23]Quanquan Gu, Zhenhui Li, Jiawei Han:
Correlated multi-label feature selection. CIKM 2011: 1087-1096 - [c22]Quanquan Gu, Jiawei Han:
Towards feature selection in network. CIKM 2011: 1175-1184 - [c21]Quanquan Gu, Chris H. Q. Ding, Jiawei Han:
On Trivial Solution and Scale Transfer Problems in Graph Regularized NMF. IJCAI 2011: 1288-1293 - [c20]Quanquan Gu, Zhenhui Li, Jiawei Han:
Joint Feature Selection and Subspace Learning. IJCAI 2011: 1294-1299 - [c19]Quanquan Gu, Zhenhui Li, Jiawei Han:
Linear Discriminant Dimensionality Reduction. ECML/PKDD (1) 2011: 549-564 - [c18]Quanquan Gu, Zhenhui Li, Jiawei Han:
Generalized Fisher Score for Feature Selection. UAI 2011: 266-273 - 2010
- [c17]Han Hu, Quanquan Gu, Jie Zhou:
HTF: a novel feature for general crack detection. ICIP 2010: 1633-1636 - [c16]Quanquan Gu, Jie Zhou, Chris H. Q. Ding:
Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs. SDM 2010: 199-210
2000 – 2009
- 2009
- [c15]Quanquan Gu, Jie Zhou:
Neighborhood Preserving Nonnegative Matrix Factorization. BMVC 2009: 1-10 - [c14]Han Hu, Quanquan Gu, Lei Deng, Jie Zhou:
Multiframe Motion Segmentation via Penalized MAP Estimation and Linear Programming. BMVC 2009: 1-11 - [c13]Quanquan Gu, Jie Zhou:
Subspace maximum margin clustering. CIKM 2009: 1337-1346 - [c12]Quanquan Gu, Jie Zhou:
Regular simplex criterion: A novel feature extraction criterion. ICASSP 2009: 1581-1584 - [c11]Quanquan Gu, Jie Zhou:
Two dimensional Maximum Margin Criterion. ICASSP 2009: 1621-1624 - [c10]Quanquan Gu, Jie Zhou:
Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification. ICDM 2009: 159-168 - [c9]Quanquan Gu, Jie Zhou:
Multiple Kernel Maximum Margin Criterion. ICIP 2009: 2049-2052 - [c8]Quanquan Gu, Jie Zhou:
Two Dimensional Nonnegative Matrix Factorization. ICIP 2009: 2069-2072 - [c7]Quanquan Gu, Jie Zhou:
Local Learning Regularized Nonnegative Matrix Factorization. IJCAI 2009: 1046-1051 - [c6]Quanquan Gu, Jie Zhou:
Co-clustering on manifolds. KDD 2009: 359-368 - [c5]Quanquan Gu, Jie Zhou:
Transductive Classification via Dual Regularization. ECML/PKDD (1) 2009: 439-454 - [c4]Quanquan Gu, Jie Zhou:
Local Relevance Weighted Maximum Margin Criterion for Text Classification. SDM 2009: 1136-1147 - 2008
- [c3]Quanquan Gu, Jie Zhou:
A novel similarity measure under Riemannian metric for stereo matching. ICASSP 2008: 1073-1076 - [c2]Quanquan Gu, Jie Zhou:
Belief propagation on Riemannian manifold for stereo matching. ICIP 2008: 1788-1791 - [c1]Quanquan Gu, Jie Zhou:
A similarity measure under Log-Euclidean metric for stereo matching. ICPR 2008: 1-4
Coauthor Index
[c212] [c211] [c201] [i160] [i159] [i158] [i149] [i148] [i137] [c198] [c192] [c190] [c187] [c183] [c181] [c171] [c170] [i130] [i125] [i122] [i121] [i120] [i113] [i105] [c168] [c166] [c157] [c156] [c151] [c150] [i100] [i97] [i95] [i89] [c136] [c133] [c119] [c118] [i84] [i79] [i78] [i65] [i63] [i48] [i46] [i39]
[j11] [c210] [c200] [i159] [i144] [i132] [c198] [c192] [c190] [c181] [c170] [i130] [i106] [c167] [c166] [c163] [c162] [c159] [c154] [c150] [c149] [c146] [i102] [i100] [i97] [i96] [i92] [i89] [c143] [c139] [c136] [c133] [c128] [c124] [c121] [c120] [c119] [c118] [c117] [i87] [i86] [i85] [i84] [i83] [i79] [i78] [i77] [i76] [i71] [i67] [i64] [i62] [j7] [j6] [c114] [c110] [c109] [c102] [c101] [i48] [i46] [i45] [i40] [i39] [i37] [j5] [c91] [i36] [i35] [i24] [c75] [c72] [i16] [i12] [i11] [i10] [i9] [i7]
[j12] [c209] [j10] [c197] [c194] [c185] [c179] [i129] [i127] [i117] [i111] [c165] [c155] [c147] [c145] [i99] [i94] [j8] [c142] [c141] [c140] [c132] [c131] [c127] [c116] [i82] [i81] [i74] [i73] [i72] [i68] [j6] [c107] [c105] [i56] [i42] [i41] [c93] [c89] [c88] [c85] [i31] [i25] [i22] [i21] [c74] [c73] [c67] [i17] [i9] [i3]
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the dblp computer science bibliography is funded and supported by:
