| 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 30, 2017 | 9949 | 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 | 624 | 2013 |
| R-drop: Regularized dropout for neural networks L Wu, J Li, Y Wang, Q Meng, T Qin, W Chen, M Zhang, TY Liu Advances in Neural Information Processing Systems 34, 10890-10905, 2021 | 293 | 2021 |
| 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 | 273 | 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 22, 2009 | 244 | 2009 |
| Dual supervised learning Y Xia, T Qin, W Chen, J Bian, N Yu, TY Liu International conference on machine learning, 3789-3798, 2017 | 159 | 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 | 158 | 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 29, 2016 | 134 | 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 | 122 | 2019 |
| & Liu, TY (2017). Lightgbm: A highly efficient gradient boosting decision tree G Ke, Q Meng, T Finley, T Wang, W Chen, W Ma Advances in neural information processing systems 30, 0 | 102 | |
| 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, 6155-6170, 2021 | 86* | 2021 |
| LightGBM: a highly efficient gradient boosting decision tree LK Guo, M Qi, T Finley, TF Wang, W Chen, WD Ma, QW Ye, TY Liu 31st Conference on Neural Information Processing Systems, 1-9, 2017 | 80 | 2017 |
| 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 | 73 | 2021 |
| Efficient inexact proximal gradient algorithm for nonconvex problems Q Yao, JT Kwok, F Gao, W Chen, TY Liu arXiv preprint arXiv:1612.09069, 2016 | 71 | 2016 |
| PriorGrad: Improving conditional denoising diffusion models with data-dependent adaptive prior S Lee, H Kim, C Shin, X Tan, C Liu, Q Meng, T Qin, W Chen, S Yoon, ... arXiv preprint arXiv:2106.06406, 2021 | 67 | 2021 |
| 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 | 60 | 2015 |
| Large scale private learning via low-rank reparametrization D Yu, H Zhang, W Chen, J Yin, TY Liu International Conference on Machine Learning, 12208-12218, 2021 | 57 | 2021 |
| Towards binary-valued gates for robust lstm training Z Li, D He, F Tian, W Chen, T Qin, L Wang, T Liu International Conference on Machine Learning, 2995-3004, 2018 | 57 | 2018 |
| A game-theoretic machine learning approach for revenue maximization in sponsored search D He, W Chen, L Wang, TY Liu arXiv preprint arXiv:1406.0728, 2014 | 54 | 2014 |
| Target transfer Q-learning and its convergence analysis Y Wang, Y Liu, W Chen, ZM Ma, TY Liu Neurocomputing 392, 11-22, 2020 | 48 | 2020 |
Tie-Yan LiuDistinguished Scientist, Microsoft Research AI4Science | IEEE Fellow | ACM Fellow | AAIA FellowVerified email at microsoft.com
