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

• Y. Feng and Q. Wu. Understanding robust machine learning for nonparametric regression with heavy-tailed noise. arXiv:2510.09888, 2025.
• R. Wang, Y. Feng, and A.M. Valm. A framework of multi-view machine learning for biological spectral unmixing of fluorophores with overlapping excitation and emission spectra. Briefings in Bioinformatics, 2025.
• Y. Feng and Q. Wu. Tikhonov regularization for Gaussian empirical gain maximization in RKHS is consistent. Applied and Computational Harmonic Analysis, 76:101735, 2025.
• S. Huang, Y. Feng, and Q. Wu. Fast rates of Gaussian empirical gain maximization with heavy-tailed noise. TNNLS, 33(10): 6038-6043, 2022.
• 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.

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