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2020 – today
- 2024
[j10]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)- [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 - [i152]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) - [i151]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) - [i150]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) - [i149]Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang:
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. CoRR abs/2402.08991 (2024) - [i148]Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu:
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. CoRR abs/2402.08998 (2024) - [i147]Kaixuan Ji, Jiafan He, Quanquan Gu:
Reinforcement Learning from Human Feedback with Active Queries. CoRR abs/2402.09401 (2024) - [i146]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) - [i145]Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu:
Diffusion Language Models Are Versatile Protein Learners. CoRR abs/2402.18567 (2024) - [i144]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) - [i143]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) - [i142]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) - [i141]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) - [i140]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) - [i139]Xuheng Li, Heyang Zhao, Quanquan Gu:
Feel-Good Thompson Sampling for Contextual Dueling Bandits. CoRR abs/2404.06013 (2024) - [i138]Weitong Zhang, Zhiyuan Fan, Jiafan He, Quanquan Gu:
Settling Constant Regrets in Linear Markov Decision Processes. CoRR abs/2404.10745 (2024) - [i137]Qiwei Di, Jiafan He, Quanquan Gu:
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback. CoRR abs/2404.10776 (2024) - [i136]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) - [i135]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) - [i134]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) - [i133]Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu:
Uncertainty-Aware Reward-Free Exploration with General Function Approximation. CoRR abs/2406.16255 (2024) - [i132]Xiwei Cheng, Xiangxin Zhou, Yuwei Yang, Yu Bao, Quanquan Gu:
Decomposed Direct Preference Optimization for Structure-Based Drug Design. CoRR abs/2407.13981 (2024) - 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