Quanquan Gu

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Dongruo ZhouUCLAVerified email at cs.ucla.edu
Jiawei HanAbel Bliss Professor of Computer Science, University of IllinoisVerified email at cs.uiuc.edu
Difan ZouThe University of Hong KongVerified email at cs.hku.hk
Pan XuDuke UniversityVerified email at duke.edu
Yuan CaoThe University of Hong KongVerified email at hku.hk
Jinghui ChenAssistant Professor of Information Sciences and Technology, Penn State UniversityVerified email at psu.edu
Yizhou SunAssociate Professor, Computer Science, UCLAVerified email at cs.ucla.edu
Spencer FreiUC BerkeleyVerified email at berkeley.edu
Tong ZhangHKUSTVerified email at tongzhang-ml.org
Jian MaRay and Stephanie Lane Professor of Computational Biology, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Vladimir BravermanVictor E. Cameron Professor of Computer Science, Rice UniversityVerified email at rice.edu
Lihong Li (李力鸿)AmazonVerified email at amazon.com
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Yuanzhi LiAssistant Professor at CMUVerified email at andrew.cmu.edu
Panagiotis D. ChristofidesWilliam D. Van Vorst Distinguished Professor and Department Chair of Chemical Engineering, UCLAVerified email at seas.ucla.edu
Mikhail (Misha) BelkinProfessor of Data Science, Halıcıoğlu Data Science Institute, UCSDVerified email at ucsd.edu
Csaba SzepesvariDeepMind & University of AlbertaVerified email at cs.ualberta.ca
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Guido F. MontufarUCLA, Mathematics and Statistics, Max Planck Institute MiSVerified email at math.ucla.edu
Nicholas G. ReichProfessor of Biostatistics, University of Massachusetts - AmherstVerified email at umass.edu

Quanquan Gu

Associate Professor of Computer Science, UCLA

Verified email at cs.ucla.edu - Homepage

Statistical Machine Learning Nonconvex Optimization Deep Learning Theory Reinforcement Learning AI for Science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Active learning: A survey CC Aggarwal, X Kong, Q Gu, J Han, SY Philip Data classification, 599-634, 2014	3016*	2014
Generalized fisher score for feature selection Q Gu, Z Li, J Han Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial …, 2012	873	2012
Personalized entity recommendation: A heterogeneous information network approach X Yu, X Ren, Y Sun, Q Gu, B Sturt, U Khandelwal, B Norick, J Han Proceedings of the 7th ACM international conference on Web search and data …, 2014	745	2014
Gradient descent optimizes over-parameterized deep ReLU networks D Zou, Y Cao, D Zhou, Q Gu Machine Learning, 1-26, 2019	578*	2019
Improving adversarial robustness requires revisiting misclassified examples Y Wang, D Zou, J Yi, J Bailey, X Ma, Q Gu International Conference on Learning Representations, 2020	444	2020
Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs Q Gu, J Zhou, C Ding Proceedings of the 2010 SIAM international conference on data mining, 199-210, 2010	301	2010
Generalization bounds of stochastic gradient descent for wide and deep neural networks Y Cao, Q Gu Advances in neural information processing systems, 2019	294	2019
On the Convergence and Robustness of Adversarial Training Y Wang, X Ma, J Bailey, J Yi, B Zhou, Q Gu International Conference on Machine Learning 1, 2, 2019	282	2019
Co-clustering on manifolds Q Gu, J Zhou Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	280	2009
Joint feature selection and subspace learning Q Gu, Z Li, J Han International Joint Conference on Artificial Intelligence 22 (1), 1294, 2011	239	2011
Recommendation in heterogeneous information networks with implicit user feedback X Yu, X Ren, Y Sun, B Sturt, U Khandelwal, Q Gu, B Norick, J Han Proceedings of the 7th ACM conference on Recommender systems, 347-350, 2013	193	2013
Layer-dependent importance sampling for training deep and large graph convolutional networks D Zou, Z Hu, Y Wang, S Jiang, Y Sun, Q Gu Advances in neural information processing systems, 2019	189	2019
Stochastic nested variance reduction for nonconvex optimization D Zhou, P Xu, Q Gu Advances in Neural Information Processing Systems, 3921-3932, 2018	188*	2018
Learning a shared subspace for multi-task clustering and transductive transfer classification Q Gu, J Zhou 2009 Ninth IEEE International Conference on Data Mining, 159-168, 2009	178	2009
An improved analysis of training over-parameterized deep neural networks D Zou, Q Gu Advances in neural information processing systems, 2019	177	2019
Global convergence of Langevin dynamics based algorithms for nonconvex optimization P Xu, J Chen, D Zou, Q Gu Advances in Neural Information Processing Systems 31, 2018	170	2018
Closing the generalization gap of adaptive gradient methods in training deep neural networks J Chen, D Zhou, Y Tang, Z Yang, Y Cao, Q Gu International Joint Conference on Artificial Intelligence, 2020	164	2020
Ensemble forecasts of coronavirus disease 2019 (COVID-19) in the US EL Ray, N Wattanachit, J Niemi, AH Kanji, K House, EY Cramer, J Bracher, ... MedRXiv, 2020.08. 19.20177493, 2020	160	2020
Generalization error bounds of gradient descent for learning overparameterized deep relu networks Y Cao, Q Gu Proceedings of the AAAI Conference on Artificial Intelligence, 2020	156*	2020
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration D Zhou, L Li, Q Gu International Conference on Machine Learning, 2020	153	2020

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