Quanquan Gu

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Dongruo ZhouIndiana University BloomingtonVerified email at iu.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
Yuan CaoThe University of Hong KongVerified email at hku.hk
Pan XuDuke UniversityVerified email at duke.edu
Jinghui ChenAssistant Professor of Information Sciences and Technology, Penn State UniversityVerified email at psu.edu
Weitong ZhangAssistant Professor, SDSS, UNC Chapel HillVerified email at unc.edu
Huizhuo YuanUniversity of California, Los AngelesVerified email at ucla.edu
Yizhou SunProfessor, Computer Science, UCLAVerified email at cs.ucla.edu
Tong ZhangUIUCVerified email at tongzhang-ml.org
Vladimir BravermanProfessor of Computer Science, Johns Hopkins University; Google Research; Adjunct Professor, Rice U.Verified email at cs.jhu.edu
Sham M KakadeHarvard UniversityVerified email at seas.harvard.edu
Spencer FreiGoogle DeepMindVerified email at google.com
Jian MaRay and Stephanie Lane Professor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Yuanzhi LiAssistant Professor at CMUVerified email at andrew.cmu.edu
Lihong Li (李力鸿)AmazonVerified email at amazon.com
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Liang ZhaoEmory UniversityVerified email at emory.edu
Chenlu YeComputer Science, University of Illinois Urbana-ChampaignVerified email at illinois.edu
Mikhail (Misha) BelkinProfessor of Data Science, Halıcıoğlu Data Science Institute, CSE, UCSD, Amazon ScholarVerified email at ucsd.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, PS Yu Data classification, 599-634, 2014	3796*	2014
Generalized fisher score for feature selection Q Gu, Z Li, J Han Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial …, 2012	1126	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	958	2014
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	938	2020
Gradient descent optimizes over-parameterized deep ReLU networks D Zou, Y Cao, D Zhou, Q Gu Machine Learning, 1-26, 2019	796	2019
Self-play fine-tuning converts weak language models to strong language models Z Chen, Y Deng, H Yuan, K Ji, Q Gu arXiv preprint arXiv:2401.01335, 2024	621	2024
Generalization bounds of stochastic gradient descent for wide and deep neural networks Y Cao, Q Gu Advances in neural information processing systems, 2019	467	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	458	2019
Trustllm: Trustworthiness in large language models L Sun, Y Huang, H Wang, S Wu, Q Zhang, C Gao, Y Huang, W Lyu, ... arXiv preprint arXiv:2401.05561 3, 2024	424*	2024
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	386	2019
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration D Zhou, L Li, Q Gu International Conference on Machine Learning, 2020	334	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	319	2010
Co-clustering on manifolds Q Gu, J Zhou Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	315	2009
An improved analysis of training over-parameterized deep neural networks D Zou, Q Gu Advances in neural information processing systems, 2019	299	2019
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States EY Cramer, EL Ray, VK Lopez, J Bracher, A Brennen, ... Proceedings of the National Academy of Sciences 119 (15), e2113561119, 2022	279	2022
Towards understanding the spectral bias of deep learning Y Cao, Z Fang, Y Wu, DX Zhou, Q Gu International Joint Conference on Artificial Intelligence, 2021	279	2021
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes D Zhou, Q Gu, C Szepesvari COLT, 2021	258	2021
Joint feature selection and subspace learning Q Gu, Z Li, J Han International Joint Conference on Artificial Intelligence 22 (1), 1294, 2011	256	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	251	2013
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	250	2020

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