| 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 | 16592 | 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 | 780 | 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 | 494 | 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 | 348 | 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 | 289 | 2009 |
| 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 | 208 | 2015 |
| Dual supervised learning Y Xia, T Qin, W Chen, J Bian, N Yu, TY Liu International conference on machine learning, 3789-3798, 2017 | 184 | 2017 |
| 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 | 178 | 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 | 151 | 2019 |
| 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 | 132 | 2021 |
| 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 | 127 | 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 | 122 | 2021 |
| 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 | 111 | 2021 |
| Asychronous training of machine learning model T Wang, W Chen, TY Liu, F Gao, YE Qiwei US Patent 12,190,232, 2025 | 76 | 2025 |
| Efficient inexact proximal gradient algorithm for nonconvex problems Q Yao, JT Kwok, F Gao, W Chen, TY Liu arXiv preprint arXiv:1612.09069, 2016 | 76 | 2016 |
| SE (3) equivariant graph neural networks with complete local frames W Du, H Zhang, Y Du, Q Meng, W Chen, N Zheng, B Shao, TY Liu International Conference on Machine Learning, 5583-5608, 2022 | 70 | 2022 |
| 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 | 65 | 2015 |
| Availability attacks create shortcuts D Yu, H Zhang, W Chen, J Yin, TY Liu Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 64 | 2022 |
| How does data augmentation affect privacy in machine learning? D Yu, H Zhang, W Chen, J Yin, TY Liu Proceedings of the AAAI conference on artificial intelligence 35 (12), 10746 …, 2021 | 64 | 2021 |
| Convergence of adagrad for non-convex objectives: Simple proofs and relaxed assumptions B Wang, H Zhang, Z Ma, W Chen The Thirty Sixth Annual Conference on Learning Theory, 161-190, 2023 | 62 | 2023 |
Tie-Yan LiuPresident, Zhongguancun Academy | IEEE Fellow | ACM Fellow | AAIA Fellow確認したメール アドレス: bjzgca.edu.cn