Quanquan Gu

Cytowane przez

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i10-indeks	176	163

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116 artykułów

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Dongruo ZhouIndiana University BloomingtonZweryfikowany adres z iu.edu
Jiawei HanAbel Bliss Professor of Computer Science, University of IllinoisZweryfikowany adres z cs.uiuc.edu
Difan ZouThe University of Hong KongZweryfikowany adres z cs.hku.hk
Yuan CaoThe University of Hong KongZweryfikowany adres z hku.hk
Pan XuDuke UniversityZweryfikowany adres z duke.edu
Jinghui ChenAssistant Professor of Information Sciences and Technology, Penn State UniversityZweryfikowany adres z psu.edu
Yizhou SunAssociate Professor, Computer Science, UCLAZweryfikowany adres z cs.ucla.edu
Tong ZhangHKUSTZweryfikowany adres z tongzhang-ml.org
Vladimir BravermanVictor E. Cameron Professor of Computer Science, Rice UniversityZweryfikowany adres z rice.edu
Spencer FreiUC DavisZweryfikowany adres z ucdavis.edu
Jian MaRay and Stephanie Lane Professor of Computational Biology, Carnegie Mellon UniversityZweryfikowany adres z cs.cmu.edu
Sham M KakadeHarvard UniversityZweryfikowany adres z seas.harvard.edu
Lihong Li (李力鸿)AmazonZweryfikowany adres z amazon.com
Yuanzhi LiAssistant Professor at CMUZweryfikowany adres z andrew.cmu.edu
Panagiotis D. ChristofidesWilliam D. Van Vorst Distinguished Professor and Department Chair of Chemical Engineering, UCLAZweryfikowany adres z seas.ucla.edu
Mikhail (Misha) BelkinProfessor of Data Science, Halıcıoğlu Data Science Institute, Computer Scinece and Engineering, UCSDZweryfikowany adres z ucsd.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyZweryfikowany adres z cs.berkeley.edu
Csaba SzepesvariDeepMind & University of AlbertaZweryfikowany adres z cs.ualberta.ca
Yiwen KouUCLAZweryfikowany adres z g.ucla.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonZweryfikowany adres z cs.washington.edu

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Quanquan Gu

Associate Professor of Computer Science, UCLA

Zweryfikowany adres z cs.ucla.edu - Strona główna

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


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
Active learning: A survey CC Aggarwal, X Kong, Q Gu, J Han, SY Philip Data classification, 599-634, 2014	3203*	2014
Generalized fisher score for feature selection Q Gu, Z Li, J Han Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial …, 2012	943	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	812	2014
Gradient descent optimizes over-parameterized deep ReLU networks D Zou, Y Cao, D Zhou, Q Gu Machine Learning, 1-26, 2019	633*	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	557	2020
Generalization bounds of stochastic gradient descent for wide and deep neural networks Y Cao, Q Gu Advances in neural information processing systems, 2019	342	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	329	2019
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	306	2010
Co-clustering on manifolds Q Gu, J Zhou Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	291	2009
Joint feature selection and subspace learning Q Gu, Z Li, J Han International Joint Conference on Artificial Intelligence 22 (1), 1294, 2011	246	2011
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	245	2019
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	216	2013
An improved analysis of training over-parameterized deep neural networks D Zou, Q Gu Advances in neural information processing systems, 2019	211	2019
Stochastic nested variance reduction for nonconvex optimization D Zhou, P Xu, Q Gu Advances in Neural Information Processing Systems, 3921-3932, 2018	205*	2018
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	197	2022
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration D Zhou, L Li, Q Gu International Conference on Machine Learning, 2020	194	2020
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	186	2018
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	182	2020
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	181	2020
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	181	2009

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