Quanquan Gu
Assistant Professor
For the complete list, please see my Google Scholar Profile and DBLP Profile.
* indicates equal contribution
Publications
2021
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How Much Over-parameterization Is Sufficient to Learn Deep ReLU
Networks?
Zixiang Chen*, Yuan Cao*, Difan Zou* and Quanquan Gu, in Proc. of the 9th International Conference on Learning Representations (ICLR), 2021. [arXiv]
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Neural Thompson Sampling
Weitong Zhang, Dongruo Zhou, Lihong Li and Quanquan Gu, in Proc. of the 9th International Conference on Learning Representations (ICLR), 2021. [arXiv]
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Direction Matters: On the Implicit Regularization Effect of Stochastic Gradient Descent with Moderate Learning Rate
Jingfeng Wu, Difan Zou, Vladimir Braverman and Quanquan Gu, in Proc. of the 9th International Conference on Learning Representations (ICLR), 2021. [arXiv]
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Revisiting Membership Inference Under Realistic Assumptions
Bargav Jayaraman, Lingxiao Wang, Katherine Knipmeyer, Quanquan Gu and David Evans, 21st Privacy Enhancing Technologies Symposium (PETS), 2021. [arXiv]
2020
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Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo
Bao Wang*, Difan Zou*, Quanquan Gu, Stanley Osher, SIAM Journal on Scientific Computing, 2020. [arXiv]
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A Finite Time Analysis of Two Time-Scale Actor Critic Methods
Yue Wu, Weitong Zhang, Pan Xu and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 33, 2020. [arXiv]
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Agnostic Learning of a Single Neuron with Gradient Descent
Spencer Frei, Yuan Cao and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 33, 2020. [arXiv]
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A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks
Zixiang Chen, Yuan Cao, Quanquan Gu and Tong Zhang, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 33, 2020. [arXiv]
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Neural Contextual Bandits with UCB-Based Exploration
Dongruo Zhou, Lihong Li and Quanquan Gu, in Proc. of the 37th International Conference on Machine Learning (ICML), 2020. [arXiv]
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A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Pan Xu and Quanquan Gu, in Proc. of the 37th International Conference on Machine Learning (ICML), 2020. [arXiv]
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Optimization Theory for ReLU Neural Networks Trained with Normalization Layers
Yonatan Dukler, Quanquan Gu and Guido Montufar, in Proc. of the 37th International Conference on Machine Learning (ICML), 2020. [arXiv]
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Is Neuron Coverage a Meaningful Measure for Testing Deep Neural Networks?
Fabrice Harel-Canada, Lingxiao Wang, Muhammad Ali Gulzar, Quanquan Gu and Miryung Kim, in Proc of ACM SIGSOFT International Symposium on the Foundations of Software Engineering (ESEC/FSE), Sacramento, California, USA, 2020.
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RayS: A Ray Searching Method for Hard-label Adversarial Attack
Jinghui Chen and Quanquan Gu, in Proc of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, USA, 2020. [arXiv]
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Stochastic Nested Variance Reduction for Nonconvex Optimization
Dongruo Zhou, Pan Xu and Quanquan Gu, Journal of Machine Learning Research (JMLR), 2020.
The short version of this paper has been published in NeurIPS 2018. The journal version adds the finding local minima extension proposed in this manuscript [arXiv]
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Closing the Generalization Gap of Adaptive Gradient Methods in Training
Deep Neural Networks
Jinghui Chen, Dongruo Zhou, Yiqi Tang, Ziyan Yang, Yuan Cao and Quanquan Gu, in Proc. of the 29th International Joint Conference on Artificial Intelligence (IJCAI), Yokohama, Japan , 2020. [arXiv]
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DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM
Bao Wang, Quanquan Gu, March Boedihardjo, Lingxiao Wang, Farzin Barekat and Stanley J. Osher, In Proc of the Mathematical and Scientific Machine Learning Conference (MSML), Princeton, New Jersey, USA, 2020. [arXiv]
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Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Xiao Zhang*, Jinghui Chen*, Quanquan Gu and David Evans, In Proc of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Sicily, Italy, 2020. [arXiv]
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Accelerated Factored Gradient Descent for Low-Rank Matrix Factorization
Dongruo Zhou, Yuan Cao and Quanquan Gu, In Proc of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Sicily, Italy,, 2020.
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Stochastic Recursive Variance-Reduced Cubic Regularization Methods
Dongruo Zhou and Quanquan Gu, In Proc of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Palermo, Sicily, Italy,, 2020. [arXiv]
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On the Global Convergence of Training Deep Linear ResNets
Difan Zou, Philip M. Long and Quanquan Gu, in Proc. of the 8th International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020. [arXiv]
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Sample Efficient Policy Gradient Methods with Recursive Variance Reduction
Pan Xu, Felicia Gao and Quanquan Gu, in Proc. of the 8th International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020. [arXiv] The short version of this paper was presented in NeurIPS 2019 Optimization Foundations of Reinforcement Learning Workshop, Vancouver, Canada.
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Improving Neural Language Generation with Spectrum Control
Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang and Quanquan Gu, in Proc. of the 8th International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.
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Improving Adversarial Robustness Requires Revisiting Misclassified Examples
Yisen Wang*, Difan Zou*, Jinfeng Yi, James Bailey, Xingjun Ma and Quanquan Gu, in Proc. of the 8th International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.
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A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
Jinghui Chen, Dongruo Zhou, Jinfeng Yi and Quanquan Gu, in Proc. of the 34th AAAI Conference on Artificial Intelligence (AAAI), New York, New York, USA, 2020. [arXiv]
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Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks
Yuan Cao and Quanquan Gu, in Proc. of the 34th AAAI Conference on Artificial Intelligence (AAAI), New York, New York, USA, 2020. [arXiv]
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A Knowledge Transfer Framework for Differentially Private Sparse
Learning
Lingxiao Wang and Quanquan Gu, in Proc. of the 34th AAAI Conference on Artificial Intelligence (AAAI), New York, New York, USA, 2020. Oral presentation [arXiv]
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Rank Aggregation via Heterogeneous Thurstone Preference Models
Tao Jin*, Pan Xu*, Quanquan Gu and Farzad Farnoud, in Proc. of the 34th AAAI Conference on Artificial Intelligence (AAAI), New York, New York, USA, 2020. Oral presentation [arXiv]
2019
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Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks
Yuan Cao and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 32, Vancouver, Canada, 2019. Spotlight presentation [arXiv]
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An Improved Analysis of Training Over-parameterized Deep Neural Networks
Difan Zou and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 32, Vancouver, Canada, 2019. [arXiv]
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Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks
Spencer Frei, Yuan Cao and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 32, Vancouver, Canada, 2019. [arXiv]
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Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks
Yuan Cao and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 32, Vancouver, Canada, 2019. [arXiv]
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Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks
Difan Zou*, Ziniu Hu*, Yewen Wang, Song Jiang, Yizhou Sun and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 32, Vancouver, Canada, 2019.
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Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction
Difan Zou and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 32, Vancouver, Canada, 2019.
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Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU
Networks
Difan Zou*, Yuan Cao*, Dongruo Zhou and Quanquan Gu, Machine Learning Journal (MLJ), 2019. [arXiv]
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Stochastic Variance-Reduced Cubic Regularized Newton Methods
Dongruo Zhou, Pan Xu and Quanquan Gu, Journal of Machine Learning Research (JMLR), 2019.
The short version of this paper has been published in ICML 2018. The journal version adds the sample efficient extension proposed in this manuscript [arXiv]
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An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient
Pan Xu, Felicia Gao and Quanquan Gu, in Proc. of the 35th International Conference on Uncertainty in Artificial Intelligence (UAI), Tel Aviv, Israel, 2019. Oral presentation [arXiv]
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Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning
Lingxiao Wang and Quanquan Gu, in Proc. of the 28th International Joint Conference on Artificial Intelligence (IJCAI), Macao, China , 2019.
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Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Dongruo Zhou and Quanquan Gu, in Proc. of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, 2019. [arXiv]
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On the Convergence and Robustness of Adversarial Training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou and Quanquan Gu, in Proc. of the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, 2019. Long talk
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Learning One-hidden-layer ReLU Networks via Gradient Descent
Xiao Zhang*, Yaodong Yu*, Lingxiao Wang* and Quanquan Gu, In Proc of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan, 2019. [arXiv]
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Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics
Difan Zou, Pan Xu and Quanquan Gu, In Proc of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), Naha, Okinawa, Japan, 2019.
2018
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Stochastic Nested Variance Reduction for Nonconvex Optimization
Dongruo Zhou, Pan Xu and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 31, Montréal, Canada, 2018. Spotlight presentation [arXiv] [Slides]
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Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
Pan Xu*, Jinghui Chen*, Difan Zou and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 31, Montréal, Canada, 2018. Spotlight presentation [arXiv]
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Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima
Yaodong Yu*, Pan Xu* and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 31, Montréal, Canada, 2018. [arXiv]
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Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
Bargav Jayaraman, Lingxiao Wang, David Evans and Quanquan Gu, in Proc. of Advances in Neural Information Processing Systems (NeurIPS) 31, Montréal, Canada, 2018.
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Subsampled Stochastic Variance-Reduced Gradient Langevin Dynamics
Difan Zou*, Pan Xu* and Quanquan Gu, in Proc. of the 34th International Conference on Uncertainty in Artificial Intelligence (UAI), Monterey, California, 2018.
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Differentially Private Hypothesis Transfer Learning,
Yang Wang, Quanquan Gu and Donald Brown, in Proc. of 28th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD’18), Dublin, Ireland, 2018.
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Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase
Procrustes Flow
Xiao Zhang*, Simon S. Du* and Quanquan Gu, in Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. [arXiv]
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A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
Xiao Zhang*, Lingxiao Wang*, Yaodong Yu and Quanquan Gu, in Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.
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Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Difan Zou*, Pan Xu* and Quanquan Gu, in Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. [arXiv]
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Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen, Pan Xu, Lingxiao Wang, Jian Ma and Quanquan Gu, in Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. Long talk [arXiv]
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Continuous and Discrete-Time Accelerated Stochastic Mirror Descent for Strongly Convex Functions
Pan Xu* and Tianhao Wang* and Quanquan Gu, in Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018.
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Stochastic Variance-Reduced Cubic Regularized Newton Methods
Dongruo Zhou, Pan Xu and Quanquan Gu, in Proc. of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. [arXiv]
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Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms
Pan Xu* and Tianhao Wang* and Quanquan Gu, in Proc of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Playa Blanca, Lanzarote, Canary Islands, 2018.
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A Unified Framework for Nonconvex Low-Rank plus Sparse Matrix Recovery
Xiao Zhang* and Lingxiao Wang* and Quanquan Gu, in Proc of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Playa Blanca, Lanzarote, Canary Islands, 2018. [arXiv]
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Continuous-trait Probabilistic Model for Comparing Multi-species Functional Genomic Data
Yang Yang, Quanquan Gu, Takayo Sasaki, Rachel O’neill, David
Gilbert and Jian Ma, in Proc. of the 22nd Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2018.
2017
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Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization
Pan Xu and Jian Ma and Quanquan Gu, In Proc. of Advances in Neural Information Processing Systems (NIPS) 30, Long Beach, CA, USA, 2017.
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Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization
Jinghui Chen and Quanquan Gu, in Proc of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Halifax, Nova Scotia, Canada, 2017.
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Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference
Aditya Chaudhry, Pan Xu and Quanquan Gu, in Proc. of the 34th International Conference on Machine Learning (ICML), Sydney, Australia, 2017.
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Variance-Reduced Stochastic Gradient High-dimensional Expectation-Maximization Algorithm
Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu, in Proc. of the 34th International Conference on Machine Learning (ICML), Sydney, Australia, 2017.
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A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery
Lingxiao Wang* and Xiao Zhang* and Quanquan Gu, in Proc. of the 34th International Conference on Machine Learning (ICML), Sydney, Australia, 2017. Subsume this paper
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Robust Gaussian Graphical Model Estimation with Arbitrary Corruption
Lingxiao Wang, Quanquan Gu, in Proc. of the 34th International Conference on Machine Learning (ICML), Sydney, Australia, 2017.
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Communication-efficient Distributed Sparse Linear Discriminant Analysis
Lu Tian, Quanquan Gu, in Proc of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida, USA, 2017.
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A Unified Computational and Statistical Framework for Nonconvex Low-Rank Matrix Estimation Lingxiao Wang* and Xiao Zhang* and Quanquan Gu, in Proc of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida, USA, 2017.
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Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent
Pan Xu, Tingting Zhang and Quanquan Gu, in Proc of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida, USA , 2017.
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High-dimensional Time Series Clustering via Cross-Predictability
Dezhi Hong, Quanquan Gu and Kamin Whitehouse, in Proc of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), Fort Lauderdale, Florida, USA , 2017.
2016
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Semiparametric Differential Graph Models
Pan Xu and Quanquan Gu,
In Proc. of Advances in Neural Information Processing Systems (NIPS) 29, Barcelona, Spain, 2016.
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Identifying gene regulatory network rewiring using latent differential graphical models
Dechao Tian and Quanquan Gu and Jian Ma, Nucleic Acids Research, 2016.
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Accelerated Stochastic Block Coordinate Descent with Optimal Sampling Aston Zhang and Quanquan Gu
in Proc of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA, 2016.
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Accelerated Stochastic Block Coordinate Gradient Descent for Sparsity Constrained Nonconvex Optimization Jinghui Chen and Quanquan Gu, in Proc of the 32th International Conference on Uncertainty in Artificial Intelligence (UAI'16), New York / New Jersey, USA, 2016.
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Forward Backward Greedy Algorithms for Multi-Task Learning with Faster Rates Lu Tian, Pan Xu and Quanquan Gu, in Proc of the 32th International Conference on Uncertainty in Artificial Intelligence (UAI'16), New York / New Jersey, USA, 2016.
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Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation Huan Gui, Jiawei Han and Quanquan Gu, in Proc. of the 33th International Conference on Machine Learning (ICML), New York, USA, 2016.
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Statistical Limits of Convex Relaxations Zhaoran Wang, Quanquan Gu, and Han Liu, in Proc. of the 33th International Conference on Machine Learning (ICML), New York, USA, 2016.
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Contextual Bandits in A Collaborative Environment Qingyun Wu, Huazheng Wang, Quanquan Gu and Hongning Wang, The 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Tuscany, Italy, 2016.
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Low-Rank and Sparse Structure Pursuit via Alternating Minimization Quanquan Gu, Zhaoran Wang and Han Liu, in Proc of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, 2016. Oral presentation
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Optimal Statistical and Computational Rates for One Bit Matrix Completion Renkun Ni and Quanquan Gu, in Proc of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, 2016.
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Precision Matrix Estimation in High Dimensional Gaussian Graphical Models with Faster Rates Lingxiao Wang, Xiang Ren and Quanquan Gu, in Proc of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), Cadiz, Spain, 2016.
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Aggregating Private Sparse Learning Models Using Multi-Party Computation Lu Tian*, Bargav Jayaraman*, Quanquan Gu and David Evans, NIPS Workshop on Private Multi-Party Machine Learning, Cadiz, Spain, 2016.
2015
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High Dimensional Expectation-Maximization Algorithm: Statistical Optimization and Asymptotic Normality Zhaoran Wang, Quanquan Gu, Yang Ning, and Han Liu, in Proc. of Advances in Neural Information Processing Systems (NIPS) 28, Montreal, Quebec, Canada, 2015.
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Robust Classification of Information Networks by Consistent Graph Learning Shi Zhi, Jiawei Han, and Quanquan Gu, in Proc. of European Conf. on Machine Learning (ECML), Porto, Portugal, 2015.
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Towards a Lower Sample Complexity for Robust One-bit Compressed Sensing Rongda Zhu and Quanquan Gu, in Proc. of the 32nd International Conference on Machine Learning (ICML), Lille, France, 2015.
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Mining drug–disease relationships as a complement to medical genetics-based drug repositioning: Where a recommendation system meets genome-wide association studies, Haiping Wang, Quanquan Gu, Jia Wei, Zhiwei Cao and Qi Liu, Clinical Pharmacology & Therapeutics, 451-454, 2015. (Impact Factor: 7.9)
2014
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Sparse PCA with Oracle Property
Quanquan Gu, Zhaoran Wang and Han Liu, In Proc. of Advances in Neural Information Processing Systems (NIPS) 27, Montreal, Quebec, Canada, 2014.
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Robust Tensor Decomposition with Gross Corruption
Quanquan Gu*, Huan Gui* and Jiawei Han, In Proc. of Advances in Neural Information Processing Systems (NIPS) 27, Montreal, Quebec, Canada, 2014.
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Batch-Mode Active Learning via Error Bound Minimization
Quanquan Gu, Tong Zhang and Jiawei Han, In Proc. of the 30th Conference on Uncertainty in Artificial Intelligence (UAI), Quebec City, Quebec, Canada, 2014.
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Online Spectral Learning on a Graph with Bandit Feedback
Quanquan Gu and Jiawei Han, In Proc. of the 14th IEEE International Conference on Data Mining (ICDM), Shenzhen, China, 2014.
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A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression
Yiyi Liu, Quanquan Gu, Jack P Hou, Jiawei Han and Jian Ma, BMC Bioinformatics, 2014.
2013
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Selective Sampling on Graphs for Classification
Quanquan Gu, Charu Aggarwal, Jialu Liu and Jiawei Han, In Proc. of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Chicago, USA, 2013.
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Unsupervised Link Selection in Networks
Quanquan Gu, Charu Aggarwal and Jiawei Han, In Proc. of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), Scottsdale, AZ, 2013.
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Clustered Support Vector Machines
Quanquan Gu and Jiawei Han, In Proc. of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), Scottsdale, AZ, 2013.
2012
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Towards Active Learning on Graphs: An Error Bound Minimization Approach
Quanquan Gu and Jiawei Han, In Proc. of the 12th IEEE International Conference on Data Mining (ICDM), Brussels, Belgium, 2012.
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Selective Labeling via Error Bound Minimization
Quanquan Gu, Tong Zhang, Chris Ding and Jiawei Han, In Proc. of Advances in Neural Information Processing Systems (NIPS) 25, Lake Tahoe, Nevada, United States, 2012.
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Locality Preserving Feature Learning
Quanquan Gu, Marina Danilevsky, Zhenhui Li and Jiawei Han, In Proc. of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), La Palma, Canary Islands, 2012.
2011
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Linear Discriminant Dimensionality Reduction
Quanquan Gu, Zhenhui Li and Jiawei Han, In Proc. of the 21st European Conference on Machine Learning (ECML), Athens, Greece, 2011.
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Generalized Fisher Score for Feature Selection
Quanquan Gu, Zhenhui Li and Jiawei Han, In Proc. of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), Barcelona, Spain, 2011.
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Learning a Kernel for Multi-Task Clustering
Quanquan Gu, Zhenhui Li and Jiawei Han, In Proc. of the 25th AAAI Conference on Artificial Intelligence (AAAI), San Francisco, California, USA, 2011.
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Joint Feature Selection and Subspace Learning
Quanquan Gu, Zhenhui Li and Jiawei Han, In Proc. of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain, 2011.
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On Trivial Solution and Scale Transfer Problems in Graph Regularized NMF
Quanquan Gu, Chris Ding and Jiawei Han, In Proc. of the 22nd International Joint Conference on Artificial Intelligence (IJCAI), Barcelona, Spain, 2011.
2010 and Before
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Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs
Quanquan Gu, Jie Zhou and Chris Ding, In Proc. of the 10th SIAM International Conference on Data Mining (SDM), Columbus, OH, USA, 2010. [Code]
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Learning a Shared Subspace for Multi-Task Clustering and Transductive Transfer Classification
Quanquan Gu and Jie Zhou, In Proc. of the 9th IEEE International Conference on Data Mining (ICDM), Miami, Florida, USA, 2009. (full paper) [Code]
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Transductive Classification via Dual Regularization
Quanquan Gu and Jie Zhou, In Proc. of the 19th European Conference on Machine Learning (ECML), Bled, Slovenia, 2009.
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Co-clustering on Manifolds
Quanquan Gu and Jie Zhou, In Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Paris, France, 2009. [Code]
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Local Learning Regularized Nonnegative Matrix Factorization
Quanquan Gu and Jie Zhou, In Proc. of the 21st International Joint Conference on Artificial Intelligence (IJCAI), Pasadena, California, USA, 2009. [Code]
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Local Relevance Weighted Maximum Margin Criterion for Text Classification
Quanquan Gu and Jie Zhou, In Proc. of the 9th SIAM International Conference on Data Mining (SDM), Sparks, Nevada, USA, 2009.
Preprints
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Local and Global Inference for High Dimensional Nonparanormal Graphical Models
Quanquan Gu, Yuan Cao, Yang Ning, and Han Liu, arXiv:1502.02347, 2015.
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Sharp Computational-Statistical Phase Transitions via Oracle Computational Model
Zhaoran Wang, Quanquan Gu and Han Liu, arXiv:1512.08861, 2015.
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Communication-efficient Distributed Estimation and Inference for Transelliptical Graphical Models
Pan Xu and Lu Tian and Quanquan Gu, arXiv:1612.09297, 2016.
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Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
Jinghui Chen, Lingxiao Wang, Xiao Zhang and Quanquan Gu, arXiv:1704.06256, 2017.
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On the Convergence of Adaptive Gradient Methods for Nonconvex
Optimization
Dongruo Zhou*, Yiqi Tang*, Ziyan Yang*, Yuan Cao and Quanquan Gu, arXiv:1808.05671, 2018.
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Efficient Privacy-Preserving Stochastic Nonconvex Optimization
Lingxiao Wang, Bargav Jayaraman, David Evans and Quanquan Gu, arXiv:1910.13659, 2019.
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Towards Understanding the Spectral Bias of Deep Learning
Yuan Cao*, Zhiying Fang*, Yue Wu*, Ding-Xuan Zhou and Quanquan Gu, arXiv:1912.01198, 2019.
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Double Explore-then-Commit: Asymptotic Optimality and Beyond
Tianyuan Jin, Pan Xu, Xiaokui Xiao and Quanquan Gu, arXiv:2002.09174, 2020.
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MOTS: Minimax Optimal Thompson Sampling
Tianyuan Jin, Pan Xu, Jieming Shi, Xiaokui Xiao and Quanquan Gu, arXiv:2003.01803, 2020.
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Provably Efficient Reinforcement Learning for Discounted MDPs with
Feature Mapping
Dongruo Zhou, Jiafan He and Quanquan Gu, arXiv:2006.13165, 2020.
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Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
Spencer Frei, Yuan Cao and Quanquan Gu, arXiv:2010.00539, 2020.
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Minimax Optimal Reinforcement Learning for Discounted MDPs
Jiafan He, Dongruo Zhou and Quanquan Gu, arXiv:2010.00587, 2020.
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Do Wider Neural Networks Really Help Adversarial Robustness?
Boxi Wu*, Jinghui Chen*, Deng Cai, Xiaofei He and Quanquan Gu, arXiv:2010.01279, 2020.
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Efficient Robust Training via Backward Smoothing
Jinghui Chen, Yu Cheng, Zhe Gan, Quanquan Gu and Jingjing Liu, arXiv:2010.01278, 2020.
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Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling
Difan Zou, Pan Xu and Quanquan Gu, arXiv:2010.09597, 2020.
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Provable Multi-Objective Reinforcement Learning with Generative Models
Dongruo Zhou, Jiahao Chen and Quanquan Gu, arXiv:2011.10134, 2020.
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Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Jiafan He, Dongruo Zhou and Quanquan Gu, arXiv:2011.11566, 2020.
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Neural Contextual Bandits with Deep Representation and Shallow Exploration
Pan Xu, Zheng Wen, Handong Zhao and Quanquan Gu, arXiv:2012.01780, 2020.
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Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
Dongruo Zhou, Quanquan Gu and Csaba Szepesvari, arXiv:2012.08507, 2020.
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Provable Generalization of SGD-trained Neural Networks of Any Width in
the Presence of Adversarial Label Noise
Spencer Frei, Yuan Cao and Quanquan Gu, arXiv:2101.01152, 2021.
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Provably Efficient Reinforcement Learning with Linear Function
Approximation Under Adaptivity Constraints
Tianhao Wang, Dongruo Zhou and Quanquan Gu, arXiv:2101.02195, 2021.
Dissertation
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