Current Research Interests

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

 

 

Selected Publications

• Quantitative convergence analysis of kernel based large-margin unified machines (2020; with D.H. Xiang), Communications on Pure and Applied Analysis, accepted.

• Optimal learning with Gaussians and correntropy loss (2020; with F.S. Lv), Analysis and Applications, to appear.

• A statistical learning approach to modal regression (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 (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 (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 (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 (2018; with Y.R. Wu et al.), Proc SPIE Int Soc Opt Eng., 10577:105770J.

• Learning rates for regularized least squares ranking algorithm (2017; with Y.L. Zhao and L. Shi), Analysis and Applications, 15(6):815-836.

• Breast cancer risk prediction using electronic health records (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 (2016; with Y.R. Wu et al.), Proc SPIE Int Soc Opt Eng., 9787:978706.

• Consistency analysis of an empirical minimum error entropy algorithm (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 (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 (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" (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 (2016; with E. Burnside et al.), Academic Radiology, 23(1):62-69.

• Regularization schemes for minimum error entropy principle (2015; with T. Hu, Q. Wu and D.X. Zhou), Analysis and Applications, 13(4):437-455.

• Parameterized BLOSUM matrices for protein alignment (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 (2013; with T. Hu, Q. Wu and D.X. Zhou), Journal of Machine Learning Research, 14:377-397.