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 For the complete list, please see my Google Scholar Profile and DBLP Profile.
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Preprints


  1. Local and Global Inference for High Dimensional Nonparanormal Graphical Models
    Quanquan Gu, Yuan Cao, Yang Ning, and Han Liu, arXiv:1502.02347, 2015.

  2. Sharp Computational-Statistical Phase Transitions via Oracle Computational Model
    Zhaoran Wang, Quanquan Gu and Han Liu, arXiv:1512.08861, 2015.

  3. Communication-efficient Distributed Estimation and Inference for Transelliptical Graphical Models
    Pan Xu and Lu Tian and Quanquan Gu, arXiv:1612.09297, 2016.

  4. Robust Wirtinger Flow for Phase Retrieval with Arbitrary Corruption
    Jinghui Chen, Lingxiao Wang, Xiao Zhang and Quanquan Gu, arXiv:1704.06256, 2017.

  5. Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks
    Jinghui Chen and Quanquan Gu, arXiv:1806.06763, 2018.

  6. Finding Local Minima via Stochastic Nested Variance Reduction
    Dongruo Zhou, Pan Xu and Quanquan Gu, arXiv:1806.08782, 2018.

  7. 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.

  8. Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
    Difan Zou*, Yuan Cao*, Dongruo Zhou and Quanquan Gu, arXiv:1811.08888, 2018.

  9. A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
    Jinghui Chen, Jinfeng Yi and Quanquan Gu, arXiv:1811.10828, 2018.

  10. Sample Efficient Stochastic Variance-Reduced Cubic Regularization Methods
    Dongruo Zhou, Pan Xu and Quanquan Gu, arXiv:1811.11989, 2018.

  11. Stochastic Recursive Variance-Reduced Cubic Regularization Methods
    Dongruo Zhou and Quanquan Gu, arXiv:1901.11518, 2019.

  12. Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks
    Yuan Cao and Quanquan Gu, arXiv:1902.01384, 2019.

Selected Publications

2019

  1. 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.

  2. 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.

  3. 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]

  4. 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.

  5. 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]

  6. 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

  1. 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 [arXiv] [Slides]

  2. 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 [arXiv]

  3. 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]

  4. 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.

  5. 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.

  6. 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.

  7. 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]

  8. 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.

  9. 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]

  10. 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]

  11. 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.

  12. 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]

  13. 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.

  14. 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]

  15. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  1. Semiparametric Differential Graph Models
    Pan Xu and Quanquan Gu, In Proc. of Advances in Neural Information Processing Systems (NIPS) 29, Barcelona, Spain, 2016.

  2. Identifying gene regulatory network rewiring using latent differential graphical models
    Dechao Tian and Quanquan Gu and Jian Ma, Nucleic Acids Research, 2016.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  1. 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.

  2. 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.

  3. 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

  1. 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.

  2. 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.

  3. 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

  1. 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.

  2. 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.

  3. Learning a Kernel for Multi-Task Clustering
    Quanquan Gu, Zhenhui Li and Jiawei Han, In Proc. of the 25th Conference on Artificial Intelligence (AAAI), San Francisco, California, USA, 2011.

  4. 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.

  5. 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

  1. 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]

  2. 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]

  3. Transductive Classification via Dual Regularization
    Quanquan Gu and Jie Zhou, In Proc. of the 19th European Conference on Machine Learning (ECML), Bled, Slovenia, 2009.

  4. 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]

  5. 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]

  6. 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.

Dissertation