Active learning: A survey CC Aggarwal, X Kong, Q Gu, J Han, SY Philip Data classification, 599-634, 2014 | 3478* | 2014 |
Generalized fisher score for feature selection Q Gu, Z Li, J Han Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial …, 2012 | 1025 | 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 | 865 | 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 | 718 | 2020 |
Gradient descent optimizes over-parameterized deep ReLU networks D Zou, Y Cao, D Zhou, Q Gu Machine Learning, 1-26, 2019 | 697 | 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 | 396 | 2019 |
Generalization bounds of stochastic gradient descent for wide and deep neural networks Y Cao, Q Gu Advances in neural information processing systems, 2019 | 391 | 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 | 313 | 2010 |
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 | 298 | 2019 |
Co-clustering on manifolds Q Gu, J Zhou Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 297 | 2009 |
Joint feature selection and subspace learning Q Gu, Z Li, J Han International Joint Conference on Artificial Intelligence 22 (1), 1294, 2011 | 255 | 2011 |
Neural Contextual Bandits with Upper Confidence Bound-Based Exploration D Zhou, L Li, Q Gu International Conference on Machine Learning, 2020 | 245 | 2020 |
An improved analysis of training over-parameterized deep neural networks D Zou, Q Gu Advances in neural information processing systems, 2019 | 242 | 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 | 233 | 2013 |
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes D Zhou, Q Gu, C Szepesvari COLT, 2021 | 217 | 2021 |
Stochastic nested variance reduction for nonconvex optimization D Zhou, P Xu, Q Gu Advances in Neural Information Processing Systems, 3921-3932, 2018 | 216* | 2018 |
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 | 209 | 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 | 207 | 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 | 198 | 2020 |
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 | 189 | 2022 |