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Research Interests

Machine Learning, Learning Theory

Instructed Courses

• AMAT810 Time Series Analysis, Fall 2018
• AMAT214 Calculus of Several Variables, Fall 2018
• AMAT108 Elementary Statistics, Spring 2018
• AMAT591 Optimization Methods and Nonlinear Programming, Spring 2018
• AMAT101 Algebra and Calculus I, Fall 2017

Recent Papers

1. Y. Feng and Y. Ying. Learning with correntropy-induced losses for regression with mixture of symmetric stable noise. arXiv preprint arXiv:1803.00183, 2018. [article link]
2. Y. Feng, J. Fan, and J. Suykens. A statistical learning approach to modal regression. arXiv preprint arXiv:1702.05960, 2017. [article link]
3. H. Hang, I. Steinwart, Y. Feng, and J. Suykens. Kernel density estimation for dynamical systems. Journal of Machine Learning Research, 35:1-49, 2018. [journal link]
4. H. Hang, Y. Feng, I. Steinwart, and J. Suykens. Learning theory estimates with observations from general stationary stochastic processes. Neural Computation, 28(12):2853-2889, 2016. [journal link]
5. Y. Feng, Y. Yang, X. Huang, S. Mehrkanoon, and J. Suykens. Robust support vector machines for classification with nonconvex and smooth losses. Neural Computation, 28(6):1217-1247, 2016. [journal link]
6. Y. Feng, S.-G. Lv, H. Hang, and J. Suykens. Kernelized elastic net regularization: generalization bounds, and sparse recovery. Neural Computation, 28(3):525-562, 2016. [journal link]
7. Y. Yang, Y. Feng, X. Huang, and J. Suykens. Rank-1 tensor properties with applications to a class of tensor optimization problems. SIAM Journal on Optimization, 26(1):171-196, 2016. [journal link]
8. Y. Feng, X. Huang, L. Shi, Y. Yang, and J. Suykens. Learning with the maximum correntropy criterion induced losses for regression. Journal of Machine Learning Research, 16:993-1034, 2015. [journal link]

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