Machine Learning, Statistical Learning Theory
- AMAT363 Statistics, Spring 2020, Fall 2020, Spring 2021
- AMAT810 Time Series Analysis, Fall 2018
- AMAT591 Optimization Methods and Nonlinear Programming, Spring 2018-2020, Fall 2020, Spring 2021
- AMAT465/565 Applied Statistics, Fall 2019
- AMAT214 Calculus of Several Variables, Fall 2018, Spring 2019
- AMAT112 Calculus I, Fall 2019
- AMAT108 Elementary Statistics, Spring 2018
- AMAT101 Algebra and Calculus I, Fall 2017
- Y. Feng and Q. Wu. Tikhonov regularization for Gaussian empirical gain maximization in RKHS is consistent. Submitted, 2021.
- Y. Feng and Q. Wu. A statistical learning assessment of Huber regression. arXiv preprint arXiv:2009.12755, 2020.
- Y. Feng and Q. Wu. A framework of learning through empirical gain maximization. Neural Computation, in press.
- 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.