| Stochastic gradient descent for nonconvex learning without bounded gradient assumptions Y Lei, T Hu, G Li, K Tang IEEE Transactions on Neural Networks and Learning Systems 31 (10), 4394-4400, 2020 | 79 | 2020 |
| Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent Y Lei, Y Ying International Conference on Machine Learning, 5809-5819, 2020 | 75 | 2020 |
| Data-dependent generalization bounds for multi-class classification Y Lei, Ü Dogan, DX Zhou, M Kloft IEEE Transactions on Information Theory 65 (5), 2995-3021, 2019 | 56 | 2019 |
| Multi-class svms: From tighter data-dependent generalization bounds to novel algorithms Y Lei, Ü Dogan, A Binder, M Kloft Advances in Neural Information Processing Systems, 2026-2034, 2015 | 54 | 2015 |
| Generalization performance of radial basis function networks Y Lei, L Ding, W Zhang IEEE Transactions on Neural Networks and Learning Systems 26 (3), 551-564, 2015 | 34 | 2015 |
| Localized Multiple Kernel Learning—A Convex Approach Y Lei, A Binder, U Dogan, M Kloft Proceedings of The 8th Asian Conference on Machine Learning 63, 81-96, 2016 | 25* | 2016 |
| Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions Y Lei, Y Ying International Conference on Learning Representations, 2021 | 24 | 2021 |
| Sharper Generalization Bounds for Pairwise Learning Y Lei, A Ledent, M Kloft Advances in Neural Information Processing Systems, 21236-21246, 2020 | 23 | 2020 |
| A generalization error bound for multi-class domain generalization AA Deshmukh, Y Lei, S Sharma, U Dogan, JW Cutler, C Scott arXiv preprint arXiv:1905.10392, 2019 | 22 | 2019 |
| Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions Y Lei, T Hu, K Tang Journal of Machine Learning Research 22 (25), 1−41, 2021 | 21 | 2021 |
| Norm-based generalisation bounds for deep multi-class convolutional neural networks A Ledent, W Mustafa, Y Lei, M Kloft Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 8279-8287, 2021 | 20* | 2021 |
| Stability and Generalization of Stochastic Gradient Methods for Minimax Problems Y Lei, Z Yang, T Yang, Y Ying International Conference on Machine Learning, 6175-6186, 2021 | 20 | 2021 |
| Learning rates for stochastic gradient descent with nonconvex objectives Y Lei, K Tang IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (12), 4505 …, 2021 | 19 | 2021 |
| Online pairwise learning algorithms with convex loss functions J Lin, Y Lei, B Zhang, DX Zhou Information Sciences 406, 57-70, 2017 | 18 | 2017 |
| Stability and differential privacy of stochastic gradient descent for pairwise learning with non-smooth loss Z Yang, Y Lei, S Lyu, Y Ying International Conference on Artificial Intelligence and Statistics, 2026-2034, 2021 | 17 | 2021 |
| Convergence of online mirror descent Y Lei, DX Zhou Applied and Computational Harmonic Analysis 48 (1), 343-373, 2020 | 17* | 2020 |
| Local rademacher complexity-based learning guarantees for multi-task learning N Yousefi, Y Lei, M Kloft, M Mollaghasemi, GC Anagnostopoulos The Journal of Machine Learning Research 19 (1), 1385-1431, 2018 | 17 | 2018 |
| Local rademacher complexity bounds based on covering numbers Y Lei, L Ding, Y Bi Neurocomputing 218, 320-330, 2016 | 17 | 2016 |
| Differentially private SGD with non-smooth losses P Wang, Y Lei, Y Ying, H Zhang Applied and Computational Harmonic Analysis 56, 306-336, 2022 | 16 | 2022 |
| Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping SB Lin, Y Lei, DX Zhou Journal of Machine Learning Research 20 (46), 1-36, 2019 | 16 | 2019 |
Yiming YingProfessor, Department of Mathematics and Statistics, University at Albany, SUNY在 albany.edu 的电子邮件经过验证
