Hi, I got my Ph.D. degree at UCLA Math Department, advised by Professor Guido Montufar. My research mainly focuses on Mathematical Machine Learning and Deep Learning Theory. Here is my resume.
Education
- Ph.D., Department of Mathematics, University of California, Los Angeles, Dec 2022
- B.S., School of Mathematical Sciences, Peking University, Jul 2017
Publication
Characterizing the Spectrum of the NTK via a Power Series Expansion
Michael Murray, Hui Jin, Benjamin Bowman, Guido Montufar, 2022.
Published in The Eleventh International Conference on Learning Representations (ICLR 2023).
Download [pdf].
Learning curves for Gaussian process regression with power-law priors and targets
Hui Jin, Pradeep Kr Banerjee, Guido Montúfar, 2021.
Published in The Tenth International Conference on Learning Representations (ICLR 2022).
Workshop version presented at Workshop on Bayesian Deep Learning NeurIPS, 2021.
Implicit bias of gradient descent for mean squared error regression with wide neural networks
Hui Jin, Guido Montúfar, 2020.
Journal of Machine Learning Research JMLR 24(137):1-97, 2023. Repo GitHub.
Noisy Subgraph Isomorphisms on Multiplex Networks
Hui Jin, Xie He, Yanghui Wang, Hao Li, Andrea L Bertozzi, 2019.
Published in 2019 IEEE International Conference on Big Data (Big Data).
Download [pdf].
Teaching
2021 Fall: PIC 16A Python with Applications I
Undergraduate course, UCLA, 2021
Core Python language constructs, applications, text processing, data visualization, interaction with spreadsheets and machine learning.
2020 Fall: PIC 10B Intermediate Programming
Undergraduate course, UCLA, 2020
Abstract data types and their implementation using C++ class mechanism; dynamic data structures, including linked lists, stacks, queues, trees, and hash tables; applications; object-oriented programming and software reuse; recursion; algorithms for sorting and searching.