廖振宇 - 华中科技大学 - 电子信息与通信学院

个人简介

I am now a research associated professor at Huazhong University of Science & Technology, School of Electronic Information and Communications, where I’m awarded the East Lake Youth Talent Program Fellowship. Before that, I was a Postdoctoral Researcher at the University of California, Berkeley, Department of Statistics, in 2020, hosted by Prof. Michael Mahoney. I received my Ph.D. from CentraleSupélec, University Paris-Saclay, in 2019, where I worked under the supervision of Prof. Romain Couillet and Prof. Yacine Chitour. I received my B.Sc degree in Optical & Electronic Information from Huazhong University of Science & Technology, China, in 2014, and my M.Sc. degree in Signal & Image Processing from University Paris-Saclay, France, in 2016. My research interests are broadly in (statistical) machine learning, signal processing, random matrix theory, and high-dimensional statistics.

近期论文

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M. Dereziński, Z. Liao, E. Dobriban, M. W. Mahoney, “Sparse sketches with small inversion bias”, The 34th Annual Conference on Learning Theory (COLT'2021), 2021. F. Liu, Z.Liao, J. A.K. Suykens, “Kernel regression in high dimension: Refined analysis beyond double descent”, The 24th International Conference on Artificial Intelligence and Statistics (AISTATS'2021), 2021. Z.Liao, R. Couillet, M. W. Mahoney, “Sparse Quantized Spectral Clustering” (spotlight), The Ninth International Conference on Learning Representations (ICLR'2021), 2021. Z.Liao, R. Couillet, M. W. Mahoney, “A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian Kernel, A Precise Phase Transition, and the Corresponding Double Descent”, The 34th Conference on Neural Information Processing Systems (NeurIPS'20), Vancouver, Canada, 2020. M. Dereziński, F. Liang, Z. Liao, M. W. Mahoney, “Precise expressions for random projections: Low-rank approximation and randomized Newton”, The 34th Conference on Neural Information Processing Systems (NeurIPS'20), Vancouver, Canada, 2020. Z.Liao, R. Couillet, “On Inner-product Kernels of High Dimensional Data (invited paper to special session)”, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP'19), Guadeloupe, French West Indies, 2019. Z. Liao, R. Couillet, “A Large Dimensional Analysis of Least Squares Support Vector Machines”, IEEE Transactions on Signal Processing 67 (4) (Feb. 2019), 1065-1074. (University of Paris-Saclay ED STIC Ph.D. Paper Award) X. Mai, Z. Liao, R. Couillet, “A Large Scale Analysis of Logistic Regression: Asymptotic Performance and New Insights”, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'19), Brighton, UK, 2019. R. Couillet, Z. Liao, X. Mai, “Classification Asymptotics in the Random Matrix Regime” (invited paper to special session), The 26th European Signal Processing Conference (EUSIPCO'18), Rome, Italy, 2018. Z. Liao, R. Couillet, “On the Spectrum of Random Features Maps of High Dimensional Data”, (long talk) Proceedings of the 35th International Conference on Machine Learning (ICML'18), Stockholm, Sweden, 2018. Z. Liao, R. Couillet, “The Dynamics of Learning: A Random Matrix Approach”, (long talk) Proceedings of the 35th International Conference on Machine Learning (ICML'18), Stockholm, Sweden, 2018. C. Louart, Z. Liao, R. Couillet, “A Random Matrix Approach to Neural Networks”, The Annals of Applied Probability 28 (2) (Apr. 2018), 1190-1248. Z. Liao, R. Couillet, “Random Matrices Meet Machine Learning: A Large Dimensional Analysis of LS-SVM”, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'17), New Orleans, USA, 2017.