| Taking human out of learning applications: A survey on automated machine learning Q Yao, M Wang, Y Chen, W Dai, YF Li, WW Tu, Q Yang, Y Yu https://arxiv.org/abs/1810.13306, 2018 | 591 | 2018 |
| Qplex: Duplex dueling multi-agent q-learning J Wang, Z Ren, T Liu, Y Yu, C Zhang arXiv preprint arXiv:2008.01062, 2020 | 569 | 2020 |
| Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application Y Hu, Q Da, A Zeng, Y Yu, Y Xu Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 213 | 2018 |
| Subset selection by Pareto optimization C Qian, Y Yu, ZH Zhou Advances in Neural Information Processing Systems 28, 1774-1782, 2015 | 207 | 2015 |
| Virtual-Taobao: Virtualizing real-world online retail environment for reinforcement learning JC Shi, Y Yu, Q Da, SY Chen, AX Zeng Proceedings of the AAAI Conference on Artificial Intelligence 33, 4902-4909, 2019 | 206 | 2019 |
| Bridging Machine Learning and Logical Reasoning by Abductive Learning WZ Dai, Q Xu, Y Yu, ZH Zhou Advances in Neural Information Processing Systems, 2811-2822, 2019 | 195 | 2019 |
| Diversity Regularized Ensemble Pruning N Li, Y Yu, ZH Zhou European Conference on Machine Learning, 2012 | 186 | 2012 |
| Towards Sample Efficient Reinforcement Learning. Y Yu IJCAI, 5739-5743, 2018 | 184 | 2018 |
| Stabilizing reinforcement learning in dynamic environment with application to online recommendation SY Chen, Y Yu, Q Da, J Tan, HK Huang, HH Tang Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 169 | 2018 |
| Evolutionary learning: Advances in theories and algorithms ZH Zhou, Y Yu, C Qian Springer, 2019 | 162 | 2019 |
| Multi-layered gradient boosting decision trees J Feng, Y Yu, ZH Zhou Advances in Neural Information Processing Systems, 3551-3561, 2018 | 152 | 2018 |
| Ensembling local learners ThroughMultimodal perturbation ZH Zhou, Y Yu Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 35 …, 2005 | 150 | 2005 |
| A Survey on Model-based Reinforcement Learning FM Luo, T Xu, H Lai, XH Chen, W Zhang, Y Yu arXiv preprint arXiv:2206.09328, 2022 | 138 | 2022 |
| Pareto ensemble pruning C Qian, Y Yu, ZH Zhou Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 127 | 2015 |
| Learning with augmented class by exploiting unlabeled data Q Da, Y Yu, ZH Zhou 28th AAAI Conference on Artificial Intelligence (AAAI'14), 1760-1766, 2014 | 123 | 2014 |
| An analysis on recombination in multi-objective evolutionary optimization C Qian, Y Yu, ZH Zhou Artificial Intelligence 204, 99-119, 2011 | 120 | 2011 |
| Derivative-free optimization via classification Y Yu, H Qian, YQ Hu Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 111 | 2016 |
| A new approach to estimating the expected first hitting time of evolutionary algorithms Y Yu, ZH Zhou Artificial Intelligence 172 (15), 1809-1832, 2008 | 111 | 2008 |
| On reinforcement learning for full-length game of starcraft ZJ Pang, RZ Liu, ZY Meng, Y Zhang, Y Yu, T Lu Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 4691-4698, 2019 | 105 | 2019 |
| Open-Category Classification by Adversarial Sample Generation Y Yu, WY Qu, N Li, Z Guo arXiv preprint arXiv:1705.08722, 2017 | 105 | 2017 |
Zhi-Hua ZhouNanjing University在 nju.edu.cn 的電子郵件地址已通過驗證
