Следене
Quanquan Gu
Quanquan Gu
Associate Professor of Computer Science, UCLA
Потвърден имейл адрес: cs.ucla.edu - Начална страница
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Позовавания
Позовавания
Година
Active learning: A survey
CC Aggarwal, X Kong, Q Gu, J Han, SY Philip
Data classification, 599-634, 2014
3206*2014
Generalized fisher score for feature selection
Q Gu, Z Li, J Han
Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial …, 2012
9442012
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
8122014
Gradient descent optimizes over-parameterized deep ReLU networks
D Zou, Y Cao, D Zhou, Q Gu
Machine Learning, 1-26, 2019
634*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
5602020
Generalization bounds of stochastic gradient descent for wide and deep neural networks
Y Cao, Q Gu
Advances in neural information processing systems, 2019
3442019
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
3312019
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
3072010
Co-clustering on manifolds
Q Gu, J Zhou
Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009
2912009
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
2472019
Joint feature selection and subspace learning
Q Gu, Z Li, J Han
International Joint Conference on Artificial Intelligence 22 (1), 1294, 2011
2462011
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
2162013
An improved analysis of training over-parameterized deep neural networks
D Zou, Q Gu
Advances in neural information processing systems, 2019
2122019
Stochastic nested variance reduction for nonconvex optimization
D Zhou, P Xu, Q Gu
Advances in Neural Information Processing Systems, 3921-3932, 2018
205*2018
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration
D Zhou, L Li, Q Gu
International Conference on Machine Learning, 2020
1942020
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
1932022
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
1862018
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
1822020
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
1812020
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
1812009
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