My research interests include learning theory, statistical machine learning, and deep neural networks.

* [Quantitative convergence analysis of kernel based large-margin unified machines](https://www.aimsciences.org/journal/1534-0392) (2020; with D.H. Xiang), _Communications on Pure and Applied Analysis_, accepted. * [Optimal learning with Gaussians and correntropy loss](https://www.worldscientific.com/doi/abs/10.1142/S0219530519410124) (2020; with F.S. Lv), _Analysis and Applications_, to appear. * [A statistical learning approach to modal regression](http://jmlr.org/papers/v21/17-068.html) (2020; with Y.L. Feng and J. Suykens), _Journal of Machine Learning Research_, 21(2):1-35. * [Convergence analysis of distributed multi-penalty regularized pairwise learning](https://www.worldscientific.com/doi/abs/10.1142/S0219530519410045) (2020; with T. Hu and D.H. Xiang), _Analysis and Applications_, 18(1):109-127. * [An RKHS approach to estimate individualized treatment rules based on functional predictors](https://www.aimsciences.org/article/doi/10.3934/mfc.2019012) (2019; with F.S. Lv and L. Shi), _Mathematical Foundations of Computing_, 2(2):169-181. * [Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5961791/) (2018; with S.I. Feld et al.), _AMIA Jt Summits Transl Sci Proc._, 81-90. * [Quantifying predictive capability of electronic health records for the most harmful breast cancer](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5914175/) (2018; with Y.R. Wu et al.), _Proc SPIE Int Soc Opt Eng._, 10577:105770J. * [Learning rates for regularized least squares ranking algorithm](http://www.worldscientific.com/doi/abs/10.1142/S0219530517500063) (2017; with Y.L. Zhao and L. Shi), _Analysis and Applications_, 15(6):815-836. * [Breast cancer risk prediction using electronic health records](https://ieeexplore.ieee.org/abstract/document/8031151) (2017; with Y.R. Wu et al.), _IEEE International Conference on Healthcare Informatics (ICHI)_, 224-228. * [Discriminatory power of common genetic variants in personalized breast cancer diagnosis](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4894298/) (2016; with Y.R. Wu et al.), _Proc SPIE Int Soc Opt Eng._, 9787:978706. * [Consistency analysis of an empirical minimum error entropy algorithm](http://www.sciencedirect.com/science/article/pii/S1063520314001456) (2016; with T. Hu, Q. Wu and D.X. Zhou), _Applied and Computational Harmonic Analysis_, 41(1):164-189. * [Structure-leveraged methods in breast cancer risk prediction](http://www.jmlr.org/papers/v17/15-444.html) (2016; with Y.R. Wu et al.), _Journal of Machine Learning Research_, 17(235):1-15. * [Sparsity and error analysis of empirical feature-based regularization schemes](http://www.jmlr.org/papers/v17/11-207.html) (2016; with X. Guo and D.X. Zhou), _Journal of Machine Learning Research_, 17(89):1-34. * [Comments on "Personalized dose finding using outcome weighted learning"](http://amstat.tandfonline.com/doi/full/10.1080/01621459.2016.1244065) (2016; with M. Yuan), _Journal of the American Statistical Association_, 111(516):1524-1525. * [Comparing mammography abnormality features and genetic variants in the prediction of breast cancer in women recommended for breast biopsy](http://www.sciencedirect.com/science/article/pii/S107663321500416X) (2016; with E. Burnside et al.), _Academic Radiology_, 23(1):62-69. * [Regularization schemes for minimum error entropy principle](http://www.worldscientific.com/doi/abs/10.1142/S0219530514500110) (2015; with T. Hu, Q. Wu and D.X. Zhou), _Analysis and Applications_, 13(4):437-455. * [Parameterized BLOSUM matrices for protein alignment](http://ieeexplore.ieee.org/abstract/document/6942278/) (2015; with D.D. Song et al.), _IEEE Transactions on Computational Biology and Bioinformatics_, 12(3):686-694. * [Learning theory approach to minimum error entropy criterion](http://www.jmlr.org/papers/v14/hu13a.html) (2013; with T. Hu, Q. Wu and D.X. Zhou), _Journal of Machine Learning Research_, 14:377-397.