Research Interests
Machine learning: theory, methods, and applications
Instructed Courses
- AMAT101 Algebra and Calculus I
- AMAT108 Elementary Statistics
- AMAT112 Calculus I
- AMAT214 Calculus of Several Variables
- AMAT363 Statistics
- AMAT367 Discrete Probability
- AMAT465/565 Applied Statistics
- AMAT591 Optimization Methods and Nonlinear Programming
- AMAT592 Machine Learning
- AMAT810 Time Series Analysis
Recent Papers
- S. Huang, Y. Feng, and Q. Wu. Fast rates of Gaussian empirical gain maximization with heavy-tailed noise. TNNLS, in press.
- Y. Feng and Q. Wu. Tikhonov regularization for Gaussian empirical gain maximization in RKHS is consistent. Submitted, 2023.
- Y. Feng and Q. Wu. A statistical learning assessment of Huber regression. Journal of Approximation Theory, 273: 105660, 2022.
- Y. Feng and Q. Wu. A framework of learning through empirical gain maximization. Neural Computation, 33(6):1656-1697, 2021.
- Y. Feng. New insights into learning with correntropy based regression. Neural Computation, 33(1):157-173, 2021.
- Y. Feng and Q. Wu. Learning under (1+ε)-moment conditions. Applied and Computational Harmonic Analysis, 49(2): 495-520, 2020.
- Y. Feng, J. Fan, and J. Suykens. A statistical learning approach to modal regression. Journal of Machine Learning Research, 21(2):1-35, 2020.
- Y. Feng and Y. Ying. Learning with correntropy-induced losses for regression with mixture of symmetric stable noise. Applied and Computational Harmonic Analysis, 48(2):795-810, 2020.