FENG Yunlong

Ph.D., Assistant Professor [CV]

  • Department of Mathematics and Statistics
  • State University of New York at Albany
  • Albany, NY 12222, USA
  • Office: Earth Science Building 128D
  • Phone: +1 518 4424611
  • Email: ylfeng at albany dot edu

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]