Scholar
Zhu Li (Michael)
Google Scholar ID: Svq4CgwAAAAJ
University College London
Citations & Impact
Citations
470
H-index
9
i10-index
9
Publications
14
Co-authors
13
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Optimal rates for regularized conditional mean embedding learning, Zhu Li, Dimitri Meunier, Mattes Mollenhauer, and Arthur Gretton. Advances in Neural Information Processing Systems 35 (2022): 4433-4445. (Oral Presentation)
- Benign overfitting and noisy features, Zhu Li, Weijie J. Su, and Dino Sejdinovic. Journal of the American Statistical Association (2022): 1-13.
- Sharp Analysis of Random Fourier Features in Classification, Zhu Li. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 7, pp. 7444-7452. 2022.
- Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness, Zhu Li, Adrián Pérez-Suay, Gustau Camps-Valls, and Dino Sejdinovic. Pattern Recognition 132 (2022): 108922.
- Towards an understanding of benign overfitting in neural networks, Zhu Li, Zhi-Hua Zhou, and Arthur Gretton. arXiv preprint arXiv:2106.03212 (2021).
- Towards a unified analysis of random fourier features, Zhu Li, Jean-Francois Ton, Dino Oglic, and Dino Sejdinovic. The Journal of Machine Learning Research 22, no. 1 (2021): 4887-4937.
- Towards a unified analysis of random fourier features, Zhu Li, Jean-Francois Ton, Dino Oglic, and Dino Sejdinovic. In International conference on machine learning, pp. 3905-3914. PMLR, 2019. (Best Paper Honourable Mention)
Research Experience
- Currently a Research Fellow working with Professor Arthur Gretton in the Gatsby Unit at University College London.
Education
- Obtained a PhD from the Department of Statistics at the University of Oxford, supervised by Professor Dino Sejdinovic.
Background
- Research interests include Kernel Methods, Learning Theory, Fast Kernel Approximation, Meta Learning, and Nonparametric Statistics.
