Yunwen Lei

Yunwen Lei 

PhD,
School of Computer Science,
University of Birmingham
Edgbaston
Birmingham B15 2TT
United Kingdom
E-mail: y.lei [@] bham.ac.uk

About Me

I am a Lecturer at School of Computer Science, University of Birmingham. Previously, I was a Humboldt Research Fellow at University of Kaiserslautern, a Research Assistant Professor at Southern University of Science and Technology, and a Postdoctoral Research Fellow at City University of Hong Kong. I obtained my PhD degree in Computer Science at Wuhan University in 2014.

Research

My research interests lie in the areas of machine learning and learning theory, with emphasis on the following topics: online learning, deep learning, optimization and extreme classification. In particular, I am interested in developing and analyzing scalable optimization methods for large-scale learning problems.

Selected Journal Publications

  1. Y. Lei and Y. Ying. "Stochastic Proximal AUC Maximization". Journal of Machine Learning Research, 22(61):1-45, 2021.

  2. Y. Lei, T. Hu and K. Tang. "Generalization Performance of Multi-pass Stochastic Gradient Descent with Convex Loss Functions". Journal of Machine Learning Research, 22(25):1−41, 2021.

  3. Y. Lei, T. Hu, G. Li and K. Tang. "Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions". IEEE Transactions on Neural Networks and Learning Systems, 31(10):4394-4400, 2020.

  4. Y. Lei and D.-X. Zhou. "Convergence of Online Mirror Descent". Applied Computational and Harmonic Analysis, 48(1):343-373, 2020. Talk Slides

  5. S.-B. Lin, Y. Lei and D.-X. Zhou. "Boosted Kernel Ridge Regression: Optimal Learning Rates and Early Stopping". Journal of Machine Learning Research, 20(46):1-36, 2019.

  6. Y. Lei, U. Dogan, D.-X. Zhou and M. Kloft. "Data-dependent Generalization Bounds for Multi-class Classification". IEEE Transactions on Information Theory, 65(5): 2995-3021, 2019. Talk Slides

  7. Y. Lei and D.-X. Zhou. "Analysis of Singular Value Thresholding Algorithm for Matrix Completion". Journal of Fourier Analysis and Applications, 25 (6):2957-2972, 2019.

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

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

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

  11. J. Lin, Y. Lei, B. Zhang and D.-X. Zhou. "Online Pairwise Learning Algorithms with Convex Loss Functions". Information Sciences, 406-407(9):57-70, 2017.

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

  13. Y. Lei and Y. Ying. "Generalization Analysis of Multi-modal Metric Learning". Analysis and Applications, 14(4): 503-521, 2016.

  14. 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.

Selected Conference Publications

  1. Y. Lei, Z. Yang, T. Yang and Y. Ying. "Stability and Generalization of Stochastic Gradient Methods for Minimax Problems". In International Conference on Machine Learning, 2021. (Long Presentation acceptance rate: 3%) Talk Slides

  2. Y. Lei and Y. Ying. "Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions". In International Conference on Learning Representations, 2021. Talk Slides

  3. Z. Yang, Y. Lei, S. Lyu and Y. Ying. "Stability and Differential Privacy of Stochastic Gradient Descent for Pairwise Learning with Non-Smooth Loss". In International Conference on Artificial Intelligence and Statistics, pages 2026-2034, 2021.

  4. Y. Lei, A. Ledent and M. Kloft. "Sharper Generalization Bounds for Pairwise Learning". In Advances in Neural Information Processing Systems, pages 21236-21246, 2020. Talk Slides

  5. Y. Lei and Y. Ying. "Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descent". In International Conference on Machine Learning, pages 5809-5819, 2020. Talk Slides

  6. Y. Lei, P. Yang, K. Tang and D.-X. Zhou. "Optimal Stochastic and Online Learning with Individual Iterates". In Advances in Neural Information Processing Systems, pages 5416-5426, 2019. (Spotlight acceptance rate: 3%) Talk Slides

  7. Y. Lei and K. Tang. "Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities". In Advances in Neural Information Processing Systems, pages 1526-1536, 2018.

  8. 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.

  9. Y. Lei, A. Binder, U. Dogan and M. Kloft. "Localized Multiple Kernel Learning-A Convex Approach". In Asian Conference on Machine Learning, 63:81-96, 2016.

  10. 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.

Teaching

Referee Experience

Journal

AA, ACHA, BDR, JAT, JoC, JSAC, JMLR, SAM, TIT, TPAMI, TSP, TNNLS, MFC, MLJ, NEUCOM, NEUNET, NEURCOMP

Conference

AAAI (2019-2021), ACML (2019–2021), AISTATS (2016–2020), COLT (2018), ECML (2021), ICLR (2018–2021), ICML (2018–2021), IJCAI (2019–2021), NeurIPS (2016–2021)