PUBLICATION
PUBLICATION
Technical Reports
Two-Stage Data Synthesization: A Statistics-Driven Restricted Trade-off between Privacy and Prediction (2026; with X. Liu, S. B. Lin and D. X. Zhou)
Spectral Algorithms under Covariate Shift (2025; with Z. C. Guo and L. Shi)
Balancing Interpretability and Performance in Reinforcement Learning: An Adaptive Spectral Based Linear Approach (2025; with Q. Yi, S. B. Lin and Y. Wang)
Efficient Low-Tubal-Rank Tensor Estimation via Alternating Preconditioned Gradient Descent (2025; with Z. Liu, Z. Han, Y. Tang and Y. Wang)
Doubly robust estimation of average treatment effect revisited (2020; with K. Guo, C. Ye and L. Zhu)
2026+
Fully online functional learning with streaming data (2025; with X. Chen and Z. C. Guo), to appeart in Analysis and Applications.
2025
Nonlinear functional regression by functional deep neural network with kernel embedding (with Z. Shi, L. Song, D. X. Zhou and J. Suykens), Journal of Machine Learning Research, 26(284):1-49.
Nyström subsampling for functional linear regression (with J. Liu and L. Shi), Journal of Approximation Theory, 310:106176.
Online outcome weighted learning with general loss functions (with A. Yang and D. H. Xiang), Journal of Complexity, 88:101931.
2024
On the convergence of gradient descent for robust functional linear regression (with C. Wang), Journal of Complexity, 84:101858.
Distributed gradient descent for functional learning (with Z. Yu, Z. Shi and D. X. Zhou), IEEE Transactions on Information Theory, 70(9):6547-6571.
Spectral algorithms for functional linear regression (with Z. C. Guo and L. Shi), Communications on Pure and Applied Analysis, 23(7):895-915.
Generalization analysis of deep CNNs under maximum correntropy criterion (with Y. Zhang and Z. Fang), Neural Networks, 174:106226.
High-probability generalization bounds for pointwise uniformly stable algorithms (with Y. Lei), Applied and Computational Harmonic Analysis, 70:101632.
Optimal prediction for kernel-based semi-functional linear regression (with K. Guo and L. Zhu), Analysis and Applications, 22(03):467-505.
Learning Korobov functions by correntropy and convolutional neural networks (with Z. Fang and T. Mao), Neural Computation, 36(4):718-743.
2023
Approximation of smooth functionals using deep ReLU networks (with L. H. Song, Y. Liu and D. X. Zhou), Neural Networks, 166:424-436.
Approximation of nonlinear functionals using deep ReLU networks (with L. H. Song, D. R. Chen and D. X. Zhou), Journal of Fourier Analysis and Applications, 29:50.
2022
Online gradient descent algorithms for functional data learning (with X. M. Chen, B. H. Tang and X. Guo), Journal of Complexity, 70:101635.
2021
Comparison theorems on large-margin learning (with A. Benabid and D.H. Xiang), International Journal of Wavelets, Multiresolution and Information Processing, 19(5):2150015.
Optimal learning with Gaussians and correntropy loss (with F.S. Lv), Analysis and Applications, 19(1):107-124.
2020
Quantitative convergence analysis of kernel based large-margin unified machines (with D.H. Xiang), Communications on Pure and Applied Analysis, 19(8):4069-4083.
A statistical learning approach to modal regression (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 (with T. Hu and D.H. Xiang), Analysis and Applications, 18(1):109-127.
2019
An RKHS approach to estimate individualized treatment rules based on functional predictors (with F.S. Lv and L. Shi), Mathematical Foundations of Computing, 2(2):169-181.
2018
Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age (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 (with Y.R. Wu et al.), Proc SPIE Int Soc Opt Eng., 10577:105770J.
2017
Learning rates for regularized least squares ranking algorithm (with Y.L. Zhao and L. Shi), Analysis and Applications, 15(6):815-836.
Breast cancer risk prediction using electronic health records (with Y.R. Wu et al.), IEEE International Conference on Healthcare Informatics (ICHI), 224-228.
2016
Discriminatory power of common genetic variants in personalized breast cancer diagnosis (with Y.R. Wu et al.), Proc SPIE Int Soc Opt Eng., 9787:978706.
Consistency analysis of an empirical minimum error entropy algorithm (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 (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 (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" (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 (with E. Burnside et al.), Academic Radiology, 23(1):62-69.
2015
Regularization schemes for minimum error entropy principle (with T. Hu, Q. Wu and D.X. Zhou), Analysis and Applications, 13(4):437-455.
Parameterized BLOSUM matrices for protein alignment (with D.D. Song et al.), IEEE Transactions on Computational Biology and Bioinformatics, 12(3):686-694.
2013
Learning theory approach to minimum error entropy criterion (with T. Hu, Q. Wu and D.X. Zhou), Journal of Machine Learning Research, 14:377-397.