• (with X. Huang, Y. Xia, Y. Huang, M. Yan, J. Hornegger and A. Maier) Mixed one-bit compressive sensing with application to overexposure correction for CT reconstruction, arXiv:1701.00694. Submitted.
  • (with Z.-C. Guo) Optimal rates for coefficient-based regularized regression. Submitted.
  • (with Z.-C. Guo and S.-B. Lin) Distributed learning with multi-penalty regularization. Submitted.
  • (with X. Huang, M. Yan and JAK. Suykens) Pinball loss minimization for one-bit compressive sensing, arXiv:1505.3898. Submitted.
  • (with X. Huang and JAK. Suykens) Sparse kernel regression with Coefficient-based lq regularization. Submitted.
  • (with J. Fan and Y. Zhao) Learning rates for regularized least squares ranking algorithm, accepted by Analysis and Applications.
  • (with X. Huang and JAK Suykens) Solution Path for pin-SVM Classifiers with positive and negative τ value, to appear in IEEE Transactions on Neural Networks and Learning Systems.

Published papers

  • (with X. Huang and M. Yan) Nonconvex sorted l1 minimization for sparse approximation, Journal of the Operations Research Society of China, 3 (2015), 207-229.
  • (with Y. Feng, X. Huang, Y. Yang and JAK. Suykens) Learning with the maximum correntropy criterion induced losses for regression, Journal of Machine Learning Research, 16 (2015), 993-1034.
  • (with X. Huang, Y. Liu, S. Van Huffel and JAK. Suykens) Two-level l1 minimization for compressed sensing, Signal Processing, 108 (2015), 459-475 .
  • (with X. Huang and JAK. Suykens) Sequential minimal optimization for SVM with pinball loss, Neurocomputing,149 (2015), 1596-1603.
  • (with X. Huang, K. Pelckmans and JAK. Suykens) Asymmetric nu-tube support vector regression, Computational Statistics & Data Analysis, 77 (2014),371-382.
  • (with X. Huang and JAK. Suykens) Ramp loss linear programming support vector machine, Journal of Machine Learning Research, 15 (2014), 2185-2211.
  • (with X. Huang and JAK. Suykens) Support vector machine classifier with pinball loss, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (2014), 984-997.
  • (with X. Huang, Z. Tian and JAK. Suykens) Quantile regression with l1-regularization and Gaussian kernels, Advances in Computational Mathematics, 40 (2014), 517-551.
  • (with X. Huang and JAK. Suykens) Asymmetric least squares support vector machine classifiers, Computational Statistics & Data Analysis, 70 (2014), 395-405.
  • (with Z.-C. Guo) Learning with coefficient-based regularization and l1-penalty,  Advances in Computational Mathematics, 39 (2013), 493-510.
  • Learning theory estimates for coefficient-based regularized regression, Applied and Computational Harmonic Analysis,34 (2013), 252-265.
  • (with X.-J. Zhou and D.-X. Zhou) Non-uniform randomized sampling for multivariate approximation by high order Parzen windows, Canadian Mathematical Bulletin,54 (2011), 566-576.
  • (with Y. Feng and D.-X. Zhou) Concentration estimates for learning with l1-regularizer and data dependent hypothesis spaces, Applied and Computational Harmonic Analysis,31 (2011), 286-302.
  • (with Z.-C. Guo) Classification with non-i.i.d. sampling, Mathematical and Computer Modelling, 54 (2011), 1347-1364.
  • (with D.-X. Zhou) Normal estimation on manifolds by gradient learning, Numerical Linear Algebra with Applications,18 (2011), 249-259.
  • (with S. Lv) Learning theory viewpoint of approximation by positive linear operators, Computers and Mathematics with Applications, 60 (2010), 3177-3186.
  • (with X. Guo and D.-X. Zhou) Hermite learning with gradient data, Journal of Computational and Applied Mathematics, 233 (2010), 3046-3059.