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
  • Tel.: +1 518 4424611
  • Email: ylfeng at albany dot edu


  • AMAT 108 - Elementary Statistics, Spring 2018
  • AMAT 591 - Optimization Methods and Nonlinear Programming, Spring 2018
  • AMAT 101 - Algebra and Calculus I, Fall 2017

Research Interests

My research interests lie in the areas of machine learning and statistical learning theory, with recent emphasis on the following topics: robust learning, kernel methods, tensor-based learning, and learning with non-i.i.d observations. In particular, I am interested in developing applicable learning algorithms with theoretical performance guarantees. The long-term goal of my research is to develop generic tools to advance big and complex data analytics.

Recent Papers

  1. Y. Feng, J. Fan, and J. Suykens. A statistical learning approach to modal regression. arXiv preprint arXiv:1702.05960, 2017.
  2. H. Hang, I. Steinwart, Y. Feng, and J. Suykens. Kernel density estimation for dynamical systems. arXiv preprint arXiv:1607.03792, 2016.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.