# Quanquan Gu

ASSOCIATE PROFESSOR
COMPUTER SCIENCE

Engineering VI - Room 491A

Email: qgu@cs.ucla.edu
Phone: (310) 794-4950
Fax: (310) 825-2273

Websites

- Quanquan Gu

##### RESEARCH AND INTERESTS

- Machine learning
- Data mining and optimization algorithms
- Computational genomic

##### NOTABLE PUBLICATIONS

For the complete list, please see my Google Scholar Profile and DBLP Profile.
* indicates equal contribution

## 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. Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks Jinghui Chen and Quanquan Gu, arXiv:1806.06763, 2018.
5. Finding Local Minima via Stochastic Nested Variance Reduction Dongruo Zhou, Pan Xu and Quanquan Gu, arXiv:1806.08782, 2018.
6. 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.
7. Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou*, Yuan Cao*, Dongruo Zhou and Quanquan Gu, arXiv:1811.08888, 2018.
8. Stochastic Recursive Variance-Reduced Cubic Regularization Methods Dongruo Zhou and Quanquan Gu, arXiv:1901.11518, 2019.
9. Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks Yuan Cao and Quanquan Gu, arXiv:1902.01384, 2019.
10. Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao and Quanquan Gu, arXiv:1905.13210, 2019.
11. An Improved Analysis of Training Over-parameterized Deep Neural Networks Difan Zou and Quanquan Gu, arXiv:1906.04688, 2019.

## Selected Publications

### 2019

1. Stochastic Variance-Reduced Cubic Regularized Newton Methods Dongruo Zhou, Pan Xu and Quanquan Gu, Accepted by the 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]
2. 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. [arXiv]
3. 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.
4. 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]
5. 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.
6. 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]
7. 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

- Online and Active Learning of Big Networks: Theory and Algorithms, Ph. D. Dissertation, University of Illinois at Urbana-Champaign, August, 2014.

##### IN THE NEWS

- AGI Blueprint? UCLA Researchers Open-Source SPIN—A Self-Improving Language Model | Hackernoon
- What Will Cold-and-Flu Season Mean for the Coronavirus Pandemic? | The New Yorker
- Models used to predict how coronavirus will spread in San Diego County, statewide | CBS8
- UCLA Machine Learning Model is Helping CDC Predict Spread of COVID-19 | Datanami
- UCLA machine-learning model is helping CDC predict spread of COVID-19 | Mirage News

##### EDUCATION

PhD (2014) University of Illinois at Urbana-Champaign

##### AWARDS AND RECOGNITION

- Sloan Research Fellowship
- AWS Machine Learning Research Award
- Yahoo! Academic Career Enhancement Award
- NSF CAREER Award
- Simons Berkeley Research Fellowship
- Salesforce Deep Learning Research Award
- Adobe Data Science Research Award