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Quanquan Gu
Quanquan Gu
Associate Professor of Computer Science, UCLA
Bestätigte E-Mail-Adresse bei cs.ucla.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Active learning: A survey
CC Aggarwal, X Kong, Q Gu, J Han, SY Philip
Data classification, 599-634, 2014
3585*2014
Generalized fisher score for feature selection
Q Gu, Z Li, J Han
Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial …, 2012
10712012
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
9152014
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
8282020
Gradient descent optimizes over-parameterized deep ReLU networks
D Zou, Y Cao, D Zhou, Q Gu
Machine Learning, 1-26, 2019
7482019
Generalization bounds of stochastic gradient descent for wide and deep neural networks
Y Cao, Q Gu
Advances in neural information processing systems, 2019
4412019
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
4282019
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
3472019
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
3172010
Co-clustering on manifolds
Q Gu, J Zhou
Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009
3042009
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration
D Zhou, L Li, Q Gu
International Conference on Machine Learning, 2020
2782020
An improved analysis of training over-parameterized deep neural networks
D Zou, Q Gu
Advances in neural information processing systems, 2019
2722019
Neural Thompson Sampling
W Zhang, D Zhou, L Li, Q Gu
International Conference on Learning Representations, 2021
2652021
Joint feature selection and subspace learning
Q Gu, Z Li, J Han
International Joint Conference on Artificial Intelligence 22 (1), 1294, 2011
2562011
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
2432013
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes
D Zhou, Q Gu, C Szepesvari
COLT, 2021
2382021
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
2352021
Position: TrustLLM: Trustworthiness in large language models
Y Huang, L Sun, H Wang, S Wu, Q Zhang, Y Li, C Gao, Y Huang, W Lyu, ...
International Conference on Machine Learning, 20166-20270, 2024
233*2024
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
2322022
Stochastic nested variance reduction for nonconvex optimization
D Zhou, P Xu, Q Gu
Advances in Neural Information Processing Systems, 3921-3932, 2018
231*2018
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