| Lightgbm: A highly efficient gradient boosting decision tree G Ke, Q Meng, T Finley, T Wang, W Chen, W Ma, Q Ye, TY Liu Advances in neural information processing systems, 3146-3154, 2017 | 7087 | 2017 |
| A theoretical analysis of NDCG type ranking measures Y Wang, L Wang, Y Li, D He, W Chen, TY Liu Conference on learning theory, 25-54, 2013 | 539 | 2013 |
| Asynchronous stochastic gradient descent with delay compensation S Zheng, Q Meng, T Wang, W Chen, N Yu, ZM Ma, TY Liu International Conference on Machine Learning, 4120-4129, 2017 | 232 | 2017 |
| Ranking measures and loss functions in learning to rank W Chen, TY Liu, Y Lan, ZM Ma, H Li Advances in Neural Information Processing Systems, 315-323, 2009 | 221 | 2009 |
| R-Drop: Regularized Dropout for Neural Networks L Wu, J Li, Y Wang, Q Meng, T Qin, W Chen, M Zhang, TY Liu Thirty-Fifth Conference on Neural Information Processing Systems, 2021 | 155 | 2021 |
| Dual supervised learning Y Xia, T Qin, W Chen, J Bian, N Yu, TY Liu arXiv preprint arXiv:1707.00415, 2017 | 148 | 2017 |
| On the depth of deep neural networks: A theoretical view S Sun, W Chen, L Wang, X Liu, TY Liu arXiv preprint arXiv:1506.05232, 2015 | 137 | 2015 |
| A communication-efficient parallel algorithm for decision tree Q Meng, G Ke, T Wang, W Chen, Q Ye, ZM Ma, TY Liu Advances in Neural Information Processing Systems, 1279-1287, 2016 | 115 | 2016 |
| Convergence analysis of distributed stochastic gradient descent with shuffling Q Meng, W Chen, Y Wang, ZM Ma, TY Liu Neurocomputing 337, 46-57, 2019 | 100 | 2019 |
| Efficient inexact proximal gradient algorithm for nonconvex problems Q Yao, JT Kwok, F Gao, W Chen, TY Liu arXiv preprint arXiv:1612.09069, 2016 | 64 | 2016 |
| Sponsored search auctions: Recent advances and future directions T Qin, W Chen, TY Liu ACM Transactions on Intelligent Systems and Technology (TIST) 5 (4), 1-34, 2015 | 55 | 2015 |
| Towards binary-valued gates for robust lstm training Z Li, D He, F Tian, W Chen, T Qin, L Wang, TY Liu arXiv preprint arXiv:1806.02988, 2018 | 53 | 2018 |
| A game-theoretic machine learning approach for revenue maximization in sponsored search D He, W Chen, L Wang, TY Liu Twenty-Third International Joint Conference on Artificial Intelligence, 2013 | 52 | 2013 |
| Learning Causal Semantic Representation for Out-of-Distribution Prediction C Liu, X Sun, J Wang, H Tang, T Li, T Qin, W Chen, TY Liu Advances in Neural Information Processing Systems 34, 2021 | 43 | 2021 |
| Do not let privacy overbill utility: Gradient embedding perturbation for private learning D Yu, H Zhang, W Chen, TY Liu arXiv preprint arXiv:2102.12677, 2021 | 43 | 2021 |
| Finite sample analysis of the GTD policy evaluation algorithms in Markov setting Y Wang, W Chen, Y Liu, ZM Ma, TY Liu Advances in Neural Information Processing Systems, 5504-5513, 2017 | 38 | 2017 |
| Asynchronous stochastic gradient descent with delay compensation for distributed deep learning S Zheng, Q Meng, T Wang, W Chen, N Yu, Z Ma, TY Liu arXiv preprint arXiv:1609.08326, 2016 | 37 | 2016 |
| Target transfer q-learning and its convergence analysis Y Wang, Y Liu, W Chen, ZM Ma, TY Liu Neurocomputing, 2020 | 33 | 2020 |
| Ensemble-compression: A new method for parallel training of deep neural networks S Sun, W Chen, J Bian, X Liu, TY Liu Joint European Conference on Machine Learning and Knowledge Discovery in …, 2017 | 30 | 2017 |
| Large Scale Private Learning via Low-rank Reparametrization D Yu, H Zhang, W Chen, J Yin, TY Liu arXiv preprint arXiv:2106.09352, 2021 | 27 | 2021 |
Tie-Yan LiuDistinguished Scientist, Microsoft Research AI4Science | IEEE Fellow | ACM Fellow | AAIA FellowVerified email at microsoft.com
