| 2026 | COLT | Avoiding exp(k | Tianyuan Jin, Heyang Zhao, Vincent Y. F. Tan, Quanquan Gu |
| 2025 | AISTATS | On the Power of Multitask Representation Learning with Gradient Descent. | Qiaobo Li, Zixiang Chen, Yihe Deng, Yiwen Kou, Yuan Cao, Quanquan Gu |
| 2025 | CVPR | LLaVA-Critic: Learning to Evaluate Multimodal Models. | Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, Chunyuan Li |
| 2025 | ICLR | Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers. | Runjia Li, Qiwei Di, Quanquan Gu |
| 2025 | ICLR | DPLM-2: A Multimodal Diffusion Protein Language Model. | Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu |
| 2025 | ICLR | Self-Play Preference Optimization for Language Model Alignment. | Yue Wu, Zhiqing Sun, Huizhuo Yuan, Kaixuan Ji, Yiming Yang, Quanquan Gu |
| 2025 | ICLR | ProteinBench: A Holistic Evaluation of Protein Foundation Models. | Fei Ye, Zaixiang Zheng, Dongyu Xue, Yuning Shen, Lihao Wang, Yiming Ma, Yan Wang, Xinyou Wang, Xiangxin Zhou, Quanquan Gu |
| 2025 | ICLR | Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis. | Zikun Zhang, Zixiang Chen, Quanquan Gu |
| 2025 | ICLR | Energy-Weighted Flow Matching for Offline Reinforcement Learning. | Shiyuan Zhang, Weitong Zhang, Quanquan Gu |
| 2025 | ICLR | Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration. | Heyang Zhao, Xingrui Yu, David Mark Bossens, Ivor W. Tsang, Quanquan Gu |
| 2025 | ICLR | CryoFM: A Flow-based Foundation Model for Cryo-EM Densities. | Yi Zhou, Yilai Li, Jing Yuan, Quanquan Gu |
| 2025 | ICML | Ranking with Multiple Oracles: From Weak to Strong Stochastic Transitivity. | Tao Jin, Yue Wu, Quanquan Gu, Farzad Farnoud |
| 2025 | ICML | An All-Atom Generative Model for Designing Protein Complexes. | Ruizhe Chen, Dongyu Xue, Xiangxin Zhou, Zaixiang Zheng, Xiangxiang Zeng, Quanquan Gu |
| 2025 | ICML | Global Convergence and Rich Feature Learning in L-Layer Infinite-Width Neural Networks under μ Parametrization. | Zixiang Chen, Greg Yang, Qingyue Zhao, Quanquan Gu |
| 2025 | ICML | Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback. | Qiwei Di, Jiafan He, Quanquan Gu |
| 2025 | ICML | Elucidating the Design Space of Multimodal Protein Language Models. | Cheng-Yen Hsieh, Xinyou Wang, Daiheng Zhang, Dongyu Xue, Fei Ye, Shujian Huang, Zaixiang Zheng, Quanquan Gu |
| 2025 | ICML | MARS: Unleashing the Power of Variance Reduction for Training Large Models. | Huizhuo Yuan, Yifeng Liu, Shuang Wu, Xun Zhou, Quanquan Gu |
| 2025 | ICML | Beyond Bradley-Terry Models: A General Preference Model for Language Model Alignment. | Yifan Zhang, Ge Zhang, Yue Wu, Kangping Xu, Quanquan Gu |
| 2025 | ICML | Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance. | Linxi Zhao, Yihe Deng, Weitong Zhang, Quanquan Gu |
| 2025 | ICML | Logarithmic Regret for Online KL-Regularized Reinforcement Learning. | Heyang Zhao, Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang |
| 2025 | ICML | Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling. | Xiangxin Zhou, Mingyu Li, Yi Xiao, Jiahan Li, Dongyu Xue, Zaixiang Zheng, Jianzhu Ma, Quanquan Gu |
| 2024 | EMNLP | Large Language Models Can Be Contextual Privacy Protection Learners. | 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 | ICLR | Understanding Transferable Representation Learning and Zero-shot Transfer in CLIP. | Zixiang Chen, Yihe Deng, Yuanzhi Li, Quanquan Gu |
| 2024 | ICLR | Variance-aware Regret Bounds for Stochastic Contextual Dueling Bandits. | Qiwei Di, Tao Jin, Yue Wu, Heyang Zhao, Farzad Farnoud, Quanquan Gu |
| 2024 | ICLR | Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning. | Qiwei Di, Heyang Zhao, Jiafan He, Quanquan Gu |
| 2024 | ICLR | Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs. | Kaixuan Ji, Qingyue Zhao, Jiafan He, Weitong Zhang, Quanquan Gu |
| 2024 | ICLR | Risk Bounds of Accelerated SGD for Overparameterized Linear Regression. | Xuheng Li, Yihe Deng, Jingfeng Wu, Dongruo Zhou, Quanquan Gu |
| 2024 | ICLR | How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? | Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Peter L. Bartlett |
| 2024 | ICLR | DecompOpt: Controllable and Decomposed Diffusion Models for Structure-based Molecular Optimization. | Xiangxin Zhou, Xiwei Cheng, Yuwei Yang, Yu Bao, Liang Wang, Quanquan Gu |
| 2024 | ICML | Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models. | Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, Quanquan Gu |
| 2024 | ICML | Position: TrustLLM: Trustworthiness in Large Language Models. | 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 | ICML | Feel-Good Thompson Sampling for Contextual Dueling Bandits. | Xuheng Li, Heyang Zhao, Quanquan Gu |
| 2024 | ICML | Protein Conformation Generation via Force-Guided SE(3) Diffusion Models. | Yan Wang, Lihao Wang, Yuning Shen, Yiqun Wang, Huizhuo Yuan, Yue Wu, Quanquan Gu |
| 2024 | ICML | Diffusion Language Models Are Versatile Protein Learners. | Xinyou Wang, Zaixiang Zheng, Fei Ye, Dongyu Xue, Shujian Huang, Quanquan Gu |
| 2024 | ICML | Borda Regret Minimization for Generalized Linear Dueling Bandits. | Yue Wu, Tao Jin, Qiwei Di, Hao Lou, Farzad Farnoud, Quanquan Gu |
| 2024 | ICML | Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption. | Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang |
| 2024 | ICML | Uncertainty-Aware Reward-Free Exploration with General Function Approximation. | Junkai Zhang, Weitong Zhang, Dongruo Zhou, Quanquan Gu |
| 2024 | WWW | Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems. | Zijie Huang, Jeehyun Hwang, Junkai Zhang, Jinwoo Baik, Weitong Zhang, Dominik Wodarz, Yizhou Sun, Quanquan Gu, Wei Wang |
| 2024 | UAI | Pure Exploration in Asynchronous Federated Bandits. | Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu, Huazheng Wang |
| 2023 | COLT | The Implicit Bias of Batch Normalization in Linear Models and Two-layer Linear Convolutional Neural Networks. | Yuan Cao, Difan Zou, Yuanzhi Li, Quanquan Gu |
| 2023 | COLT | Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency. | Heyang Zhao, Jiafan He, Dongruo Zhou, Tong Zhang, Quanquan Gu |
| 2023 | ICLR | A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning. | Zixiang Chen, Chris Junchi Li, Huizhuo Yuan, Quanquan Gu, Michael I. Jordan |
| 2023 | ICLR | How Does Semi-supervised Learning with Pseudo-labelers Work? A Case Study. | Yiwen Kou, Zixiang Chen, Yuan Cao, Quanquan Gu |
| 2023 | ICLR | Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization. | Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu |
| 2023 | ICLR | Understanding Train-Validation Split in Meta-Learning with Neural Networks. | Xinzhe Zuo, Zixiang Chen, Huaxiu Yao, Yuan Cao, Quanquan Gu |
| 2023 | ICML | Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path. | Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2023 | ICML | DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. | Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu |
| 2023 | ICML | Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes. | Jiafan He, Heyang Zhao, Dongruo Zhou, Quanquan Gu |
| 2023 | ICML | Benign Overfitting in Two-layer ReLU Convolutional Neural Networks. | Yiwen Kou, Zixiang Chen, Yuanzhou Chen, Quanquan Gu |
| 2023 | ICML | Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization. | Chris Junchi Li, Huizhuo Yuan, Gauthier Gidel, Quanquan Gu, Michael I. Jordan |
| 2023 | ICML | Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. | Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu |
| 2023 | ICML | Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron. | Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2023 | ICML | Personalized Federated Learning under Mixture of Distributions. | Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng |
| 2023 | ICML | Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes. | Chenlu Ye, Wei Xiong, Quanquan Gu, Tong Zhang |
| 2023 | ICML | On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits. | Weitong Zhang, Jiafan He, Zhiyuan Fan, Quanquan Gu |
| 2023 | ICML | Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs. | Junkai Zhang, Weitong Zhang, Quanquan Gu |
| 2023 | ICML | Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits. | Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu |
| 2023 | ICML | Structure-informed Language Models Are Protein Designers. | Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu |
| 2023 | ICML | The Benefits of Mixup for Feature Learning. | Difan Zou, Yuan Cao, Yuanzhi Li, Quanquan Gu |
| 2023 | UAI | Efficient Privacy-Preserving Stochastic Nonconvex Optimization. | Lingxiao Wang, Bargav Jayaraman, David Evans, Quanquan Gu |
| 2023 | UAI | Benign Overfitting in Adversarially Robust Linear Classification. | Jinghui Chen, Yuan Cao, Quanquan Gu |
| 2023 | UAI | Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension. | Yue Wu, Jiafan He, Quanquan Gu |
| 2023 | UAI | Provably efficient representation selection in Low-rank Markov Decision Processes: from online to offline RL. | Weitong Zhang, Jiafan He, Dongruo Zhou, Amy Zhang, Quanquan Gu |
| 2022 | AAAI | Efficient Robust Training via Backward Smoothing. | Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu, Jingjing Liu |
| 2022 | ACML | Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes. | Chonghua Liao, Jiafan He, Quanquan Gu |
| 2022 | AISTATS | Self-training Converts Weak Learners to Strong Learners in Mixture Models. | Spencer Frei, Difan Zou, Zixiang Chen, Quanquan Gu |
| 2022 | AISTATS | Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs. | Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2022 | AISTATS | Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons. | Yue Wu, Tao Jin, Hao Lou, Pan Xu, Farzad Farnoud, Quanquan Gu |
| 2022 | AISTATS | Nearly Minimax Optimal Regret for Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. | Yue Wu, Dongruo Zhou, Quanquan Gu |
| 2022 | ALT | Faster Perturbed Stochastic Gradient Methods for Finding Local Minima. | Zixiang Chen, Dongruo Zhou, Quanquan Gu |
| 2022 | ALT | Almost Optimal Algorithms for Two-player Zero-Sum Linear Mixture Markov Games. | Zixiang Chen, Dongruo Zhou, Quanquan Gu |
| 2022 | ICLR | Neural Contextual Bandits with Deep Representation and Shallow Exploration. | Pan Xu, Zheng Wen, Handong Zhao, Quanquan Gu |
| 2022 | ICLR | Learning Neural Contextual Bandits through Perturbed Rewards. | Yiling Jia, Weitong Zhang, Dongruo Zhou, Quanquan Gu, Hongning Wang |
| 2022 | ICLR | On the Convergence of Certified Robust Training with Interval Bound Propagation. | Yihan Wang, Zhouxing Shi, Quanquan Gu, Cho-Jui Hsieh |
| 2022 | ICML | On the Sample Complexity of Learning Infinite-horizon Discounted Linear Kernel MDPs. | Yuanzhou Chen, Jiafan He, Quanquan Gu |
| 2022 | ICML | Learning Stochastic Shortest Path with Linear Function Approximation. | Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu |
| 2022 | ICML | Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. | Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2022 | ICML | Dimension-free Complexity Bounds for High-order Nonconvex Finite-sum Optimization. | Dongruo Zhou, Quanquan Gu |
| 2021 | COLT | Double Explore-then-Commit: Asymptotic Optimality and Beyond. | Tianyuan Jin, Pan Xu, Xiaokui Xiao, Quanquan Gu |
| 2021 | COLT | Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes. | Dongruo Zhou, Quanquan Gu, Csaba Szepesvri |
| 2021 | COLT | Benign Overfitting of Constant-Stepsize SGD for Linear Regression. | Difan Zou, Jingfeng Wu, Vladimir Braverman, Quanquan Gu, Sham M. Kakade |
| 2021 | ICLR | How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? | Zixiang Chen, Yuan Cao, Difan Zou, Quanquan Gu |
| 2021 | ICLR | Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate. | Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu |
| 2021 | ICLR | Neural Thompson Sampling. | Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu |
| 2021 | ICML | Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. | Spencer Frei, Yuan Cao, Quanquan Gu |
| 2021 | ICML | Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. | Spencer Frei, Yuan Cao, Quanquan Gu |
| 2021 | ICML | Logarithmic Regret for Reinforcement Learning with Linear Function Approximation. | Jiafan He, Dongruo Zhou, Quanquan Gu |
| 2021 | ICML | MOTS: Minimax Optimal Thompson Sampling. | Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao, Quanquan Gu |
| 2021 | ICML | Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. | Tianyuan Jin, Jing Tang, Pan Xu, Keke Huang, Xiaokui Xiao, Quanquan Gu |
| 2021 | ICML | Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping. | Dongruo Zhou, Jiafan He, Quanquan Gu |
| 2021 | ICML | Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. | Difan Zou, Spencer Frei, Quanquan Gu |
| 2021 | ICML | On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. | Difan Zou, Quanquan Gu |
| 2021 | IJCAI | Towards Understanding the Spectral Bias of Deep Learning. | Yuan Cao, Zhiying Fang, Yue Wu, Ding-Xuan Zhou, Quanquan Gu |
| 2021 | NAACL | Variance-reduced First-order Meta-learning for Natural Language Processing Tasks. | Lingxiao Wang, Kevin Huang, Tengyu Ma, Quanquan Gu, Jing Huang |
| 2021 | UAI | Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. | Difan Zou, Pan Xu, Quanquan Gu |
| 2020 | AAAI | Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep ReLU Networks. | Yuan Cao, Quanquan Gu |
| 2020 | AAAI | A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks. | Jinghui Chen, Dongruo Zhou, Jinfeng Yi, Quanquan Gu |
| 2020 | AAAI | Rank Aggregation via Heterogeneous Thurstone Preference Models. | Tao Jin, Pan Xu, Quanquan Gu, Farzad Farnoud |
| 2020 | AAAI | A Knowledge Transfer Framework for Differentially Private Sparse Learning. | Lingxiao Wang, Quanquan Gu |
| 2020 | AISTATS | Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models. | Xiao Zhang, Jinghui Chen, Quanquan Gu, David Evans |
| 2020 | AISTATS | Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization. | Dongruo Zhou, Yuan Cao, Quanquan Gu |
| 2020 | AISTATS | Stochastic Recursive Variance-Reduced Cubic Regularization Methods. | Dongruo Zhou, Quanquan Gu |
| 2020 | ICLR | Sample Efficient Policy Gradient Methods with Recursive Variance Reduction. | Pan Xu, Felicia Gao, Quanquan Gu |
| 2020 | ICLR | Improving Adversarial Robustness Requires Revisiting Misclassified Examples. | Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu |
| 2020 | ICLR | Improving Neural Language Generation with Spectrum Control. | Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, Quanquan Gu |
| 2020 | ICLR | On the Global Convergence of Training Deep Linear ResNets. | Difan Zou, Philip M. Long, Quanquan Gu |
| 2020 | ICML | A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation. | Pan Xu, Quanquan Gu |
| 2020 | ICML | Optimization Theory for ReLU Neural Networks Trained with Normalization Layers. | Yonatan Dukler, Quanquan Gu, Guido Montfar |
| 2020 | ICML | Neural Contextual Bandits with UCB-based Exploration. | Dongruo Zhou, Lihong Li, Quanquan Gu |
| 2020 | IJCAI | Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. | Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao, Quanquan Gu |
| 2020 | KDD | RayS: A Ray Searching Method for Hard-label Adversarial Attack. | Jinghui Chen, Quanquan Gu |
| 2019 | AISTATS | Learning One-hidden-layer ReLU Networks via Gradient Descent. | Xiao Zhang, Yaodong Yu, Lingxiao Wang, Quanquan Gu |
| 2019 | AISTATS | Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics. | Difan Zou, Pan Xu, Quanquan Gu |
| 2019 | ICML | On the Convergence and Robustness of Adversarial Training. | Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, Quanquan Gu |
| 2019 | ICML | Lower Bounds for Smooth Nonconvex Finite-Sum Optimization. | Dongruo Zhou, Quanquan Gu |
| 2019 | IJCAI | Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning. | Lingxiao Wang, Quanquan Gu |
| 2019 | UAI | An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient. | Pan Xu, Felicia Gao, Quanquan Gu |
| 2018 | AISTATS | Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms. | Pan Xu, Tianhao Wang, Quanquan Gu |
| 2018 | AISTATS | A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery. | Xiao Zhang, Lingxiao Wang, Quanquan Gu |
| 2018 | ICDCS | Towards Personalized Learning in Mobile Sensing Systems. | Wenjun Jiang, Qi Li, Lu Su, Chenglin Miao, Quanquan Gu, Wenyao Xu |
| 2018 | ICML | Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization. | Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma, Quanquan Gu |
| 2018 | ICML | Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions. | Pan Xu, Tianhao Wang, Quanquan Gu |
| 2018 | ICML | Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow. | Xiao Zhang, Simon S. Du, Quanquan Gu |
| 2018 | ICML | A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery. | Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu |
| 2018 | ICML | Stochastic Variance-Reduced Cubic Regularized Newton Method. | Dongruo Zhou, Pan Xu, Quanquan Gu |
| 2018 | ICML | Stochastic Variance-Reduced Hamilton Monte Carlo Methods. | Difan Zou, Pan Xu, Quanquan Gu |
| 2018 | RECOMB | Continuous-Trait Probabilistic Model for Comparing Multi-species Functional Genomic Data. | Yang Yang, Quanquan Gu, Takayo Sasaki, Julianna Crivello, Rachel O'Neill, David M. Gilbert, Jian Ma |
| 2018 | UAI | Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics. | Difan Zou, Pan Xu, Quanquan Gu |
| 2017 | AISTATS | High-dimensional Time Series Clustering via Cross-Predictability. | Dezhi Hong, Quanquan Gu, Kamin Whitehouse |
| 2017 | AISTATS | Communication-efficient Distributed Sparse Linear Discriminant Analysis. | Lu Tian, Quanquan Gu |
| 2017 | AISTATS | A Unified Computational and Statistical Framework for Nonconvex Low-rank Matrix Estimation. | Lingxiao Wang, Xiao Zhang, Quanquan Gu |
| 2017 | AISTATS | Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent. | Pan Xu, Tingting Zhang, Quanquan Gu |
| 2017 | ICML | Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. | Aditya Chaudhry, Pan Xu, Quanquan Gu |
| 2017 | ICML | Robust Gaussian Graphical Model Estimation with Arbitrary Corruption. | Lingxiao Wang, Quanquan Gu |
| 2017 | ICML | A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery. | Lingxiao Wang, Xiao Zhang, Quanquan Gu |
| 2017 | ICML | High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. | Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu |
| 2017 | KDD | Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization. | Jinghui Chen, Quanquan Gu |
| 2016 | AISTATS | Low-Rank and Sparse Structure Pursuit via Alternating Minimization. | Quanquan Gu, Zhaoran Wang, Han Liu |
| 2016 | AISTATS | Optimal Statistical and Computational Rates for One Bit Matrix Completion. | Renkun Ni, Quanquan Gu |
| 2016 | AISTATS | Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates. | Lingxiao Wang, Xiang Ren, Quanquan Gu |
| 2016 | ICML | Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation. | Huan Gui, Jiawei Han, Quanquan Gu |
| 2016 | ICML | On the Statistical Limits of Convex Relaxations. | Zhaoran Wang, Quanquan Gu, Han Liu |
| 2016 | KDD | Accelerated Stochastic Block Coordinate Descent with Optimal Sampling. | Aston Zhang, Quanquan Gu |
| 2016 | SIGIR | Contextual Bandits in a Collaborative Environment. | Qingyun Wu, Huazheng Wang, Quanquan Gu, Hongning Wang |
| 2016 | UAI | Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization. | Jinghui Chen, Quanquan Gu |
| 2016 | UAI | Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates. | Lu Tian, Pan Xu, Quanquan Gu |
| 2015 | CIKM | Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks. | Chang Wan, Xiang Li, Ben Kao, Xiao Yu, Quanquan Gu, David Wai-Lok Cheung, Jiawei Han |
| 2015 | ICML | Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing. | Rongda Zhu, Quanquan Gu |
| 2015 | SDM | GIN: A Clustering Model for Capturing Dual Heterogeneity in Networked Data. | Jialu Liu, Chi Wang, Jing Gao, Quanquan Gu, Charu C. Aggarwal, Lance M. Kaplan, Jiawei Han |
| 2014 | ICDM | Online Spectral Learning on a Graph with Bandit Feedback. | Quanquan Gu, Jiawei Han |
| 2014 | KDD | ClusCite: effective citation recommendation by information network-based clustering. | Xiang Ren, Jialu Liu, Xiao Yu, Urvashi Khandelwal, Quanquan Gu, Lidan Wang, Jiawei Han |
| 2014 | UAI | Batch-Mode Active Learning via Error Bound Minimization. | Quanquan Gu, Tong Zhang, Jiawei Han |
| 2014 | WSDM | Personalized entity recommendation: a heterogeneous information network approach. | Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, Jiawei Han |
| 2013 | AISTATS | Unsupervised Link Selection in Networks. | Quanquan Gu, Charu C. Aggarwal, Jiawei Han |
| 2013 | AISTATS | Clustered Support Vector Machines. | Quanquan Gu, Jiawei Han |
| 2013 | KDD | Selective sampling on graphs for classification. | Quanquan Gu, Charu C. Aggarwal, Jialu Liu, Jiawei Han |
| 2013 | KDD | Mining lines in the sand: on trajectory discovery from untrustworthy data in cyber-physical system. | Lu-An Tang, Xiao Yu, Quanquan Gu, Jiawei Han, Alice Leung, Thomas La Porta |
| 2013 | RecSys | Recommendation in heterogeneous information networks with implicit user feedback. | Xiao Yu, Xiang Ren, Yizhou Sun, Bradley Sturt, Urvashi Khandelwal, Quanquan Gu, Brandon Norick, Jiawei Han |
| 2012 | ICDM | Towards Active Learning on Graphs: An Error Bound Minimization Approach. | Quanquan Gu, Jiawei Han |
| 2012 | SDM | IntruMine: Mining Intruders in Untrustworthy Data of Cyber-physical Systems. | Lu-An Tang, Quanquan Gu, Xiao Yu, Jiawei Han, Thomas La Porta, Alice Leung, Tarek F. Abdelzaher, Lance M. Kaplan |
| 2012 | SDM | Citation Prediction in Heterogeneous Bibliographic Networks. | Xiao Yu, Quanquan Gu, Mianwei Zhou, Jiawei Han |
| 2011 | AAAI | Learning a Kernel for Multi-Task Clustering. | Quanquan Gu, Zhenhui Li, Jiawei Han |
| 2011 | CIKM | Towards feature selection in network. | Quanquan Gu, Jiawei Han |
| 2011 | CIKM | Correlated multi-label feature selection. | Quanquan Gu, Zhenhui Li, Jiawei Han |
| 2011 | IJCAI | On Trivial Solution and Scale Transfer Problems in Graph Regularized NMF. | Quanquan Gu, Chris H. Q. Ding, Jiawei Han |
| 2011 | IJCAI | Joint Feature Selection and Subspace Learning. | Quanquan Gu, Zhenhui Li, Jiawei Han |
| 2011 | UAI | Generalized Fisher Score for Feature Selection. | Quanquan Gu, Zhenhui Li, Jiawei Han |
| 2010 | ICIP | HTF: a novel feature for general crack detection. | Han Hu, Quanquan Gu, Jie Zhou |
| 2010 | SDM | Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs. | Quanquan Gu, Jie Zhou, Chris H. Q. Ding |
| 2009 | BMVC | Neighborhood Preserving Nonnegative Matrix Factorization. | Quanquan Gu, Jie Zhou |
| 2009 | BMVC | Multiframe Motion Segmentation via Penalized MAP Estimation and Linear Programming. | Han Hu, Quanquan Gu, Lei Deng, Jie Zhou |
| 2009 | CIKM | Subspace maximum margin clustering. | Quanquan Gu, Jie Zhou |
| 2009 | ICASSP | Regular simplex criterion: A novel feature extraction criterion. | Quanquan Gu, Jie Zhou |
| 2009 | ICASSP | Two dimensional Maximum Margin Criterion. | Quanquan Gu, Jie Zhou |
| 2009 | ICDM | Learning the Shared Subspace for Multi-task Clustering and Transductive Transfer Classification. | Quanquan Gu, Jie Zhou |
| 2009 | ICIP | Multiple Kernel Maximum Margin Criterion. | Quanquan Gu, Jie Zhou |
| 2009 | ICIP | Two Dimensional Nonnegative Matrix Factorization. | Quanquan Gu, Jie Zhou |
| 2009 | IJCAI | Local Learning Regularized Nonnegative Matrix Factorization. | Quanquan Gu, Jie Zhou |
| 2009 | KDD | Co-clustering on manifolds. | Quanquan Gu, Jie Zhou |
| 2009 | SDM | Local Relevance Weighted Maximum Margin Criterion for Text Classification. | Quanquan Gu, Jie Zhou |
| 2008 | ICASSP | A novel similarity measure under Riemannian metric for stereo matching. | Quanquan Gu, Jie Zhou |
| 2008 | ICIP | Belief propagation on Riemannian manifold for stereo matching. | Quanquan Gu, Jie Zhou |
| 2008 | ICPR | A similarity measure under Log-Euclidean metric for stereo matching. | Quanquan Gu, Jie Zhou |