People

Faculty

LEI, Yunwen    Research Assistant Professor

Email:

leiyw_AT_sustech.edu.cn

Office:

Room 913, Building A7, Nanshan i-Park

Research Area:

Machine Learning, Statistical Learning Theory

Educational Background

◆ 2008-2014,Wuhan University,PhD

◆ 2004-2008,Hunan University,B.S.


Professional Experience

◆ 2017-Present,Research Assistant Professorof Computer Science and Engineering Department, Southern University of Science and Technology, Shenzhen, China

◆ 2015-2017,Postdoctoral Fellow,City University of Hong Kong, Hong Kong, China


Honors & Awards

2015: Hubei Province Excellent Doctoral Dissertation


Selected Publication

1. Y. Lei and K. Tang. “Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities.” In Advance in Neural Information Processing Systems, to appear

2. Y. Lei and D.-X. Zhou. “Convergence of Online Mirror Descent.” Applied Computational and Harmonic Analysis, 2018. doi: https://doi.org/10.1016/j.acha.2018.05.005

3. N. Yousefi, Y. Lei, M. Kloft, M. Mollaghasemi and G. Anagnostopoulos. “Local Rademacher Complexity-based Learning Guarantees for Multitask Learning.” Journal of Machine Learning Research, 19(38):1-47, 2018.

4. Y. Lei, L. Shi and Z.-C. Guo. “Convergence of Unregularized Online Learning Algorithms.” Journal of Machine Learning Research, 18(171):1-33, 2018.

5. Y. Lei and D.-X. Zhou. “Learning Theory of Randomized Sparse Kaczmarz Method.” SIAM Journal on Imaging Sciences, 11(1):547-574, 2018.

6. Y. Lei, S.-B. Lin and K. Tang. “Generalization Bounds for Regularized Pairwise Learning.” In International Joint Conference on Artificial Intelligence, pages 2376-2382, 2018.

7. Y. Lei and D.-X. Zhou. “Analysis of Online Composite Mirror Descent Algorithm.” Neural Computation, 29(3):825-860, 2017.

8. Y. Lei, A. Binder, U. Dogan and M. Kloft. “Localized Multiple Kernel Learning—A Convex Approach.” JMLR Conference and Workshop Proceedings: Asian Conference on Machine Learning, 63:81-96, 2016.

9. Y. Lei, U. Dogan, A. Binder and M. Kloft. “Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms.” In Advances in Neural Information Processing Systems, pages 2026-2034, 2015.

10. Y. Lei, L. Ding and W. Zhang. “Generalization Performance of Radial Basis Function Networks.” IEEE Transactions on Neural Networks and Learning Systems, 26(3):551-564, 2015.

11. Y. Lei and L. Ding. “Refined Rademacher Chaos Complexity Bounds with Applications to the Multi-Kernel Learning Problem.” Neural Computation, 26(4):739-760, 2014.

12. Y. Lei, L. Ding and Y. Ding. “Generalization Ability of Fractional Polynomial Models.” Neural Networks, 49(C):59-73, 2014.


Others