- Efficient kernel canonical correlation analysis using Nyström approximation (with Q. Fang, M. Xu and D.-X. Zhou),
*Inverse Problems***40**(2024), 045007 (28pp). - Coefficient-based regularized distribution regression (with Y. Mao and Z.-C. Guo),
*Journal of Approximation Theory*(2024), 1-37. - Optimality of robust online learning (with Z.-C. Guo and A. Christmann),
*Foundations of Computational Mathematics*(2023), 1-29. - Optimal gradient tracking for decentralized optimization (with Z. Song, S. Pu and M. Yan),
*Mathematical Programming*(2023), 1-53. - Capacity dependent analysis for functional online learning algorithms (with X. Guo and Z.-C. Guo),
*Applied and Computational Harmonic Analysis***67**(2023), 1-30. - Learning with asymmetric kernels: least squares and feature interpretation (with M. He, F. He, X. Huang and JAK. Suykens),
*IEEE Transactions on Pattern Analysis and Machine Intelligence***45**(2023), 10044-10054. - Distributed spectral pairwise ranking algorithms (with Z.-C. Guo and T. Hu),
*Inverse Problems***39**(2023), 025003 (21pp). - Communication-efficient topologies for decentralized learning with O(1) consensus rate (with Z. Song et al.),
*NeurIPS 2022.* - Solving parametric partial differential equations with deep rectified quadratic unit neural networks (with Z. Lei and C. Zeng),
*Journal of Scientific Computing***93**(2022), 1-31. - Compressed gradient tracking for decentralized optimization over general directed networks (with Z. Song, S. Pu and M. Yan),
*IEEE Transactions on Signal Processing***70**(2022), 1775-1787. - Regularized regression problem in hyper-RKHS for learning kernels (with F. Liu, X. Huang, J. Yang and JAK. Suykens),
*Journal of Machine Learning Research***22**(2021), 1–38. - Analysis of Regularized Least-Squares in Reproducing Kernel Krĕın Spaces (with F. Liu, X. Huang, J. Yang and JAK. Suykens),
*Machine Learning***110**(2021), 1145–1173. - Fast algorithms for robust principal component analysis with an upper bound on the rank (with N. Sha and M. Yan),
*Inverse Problems and Imaging***15**(2021), 109-128. - Realizing data features by deep nets (with Z.-C. Guo and S.-B. Lin),
*IEEE Transactions on Neural Networks and Learning Systems***31**(2020), 4036-4048. - A double-variational Bayesian framework in random Fourier features for indefinite kernels (with F. Liu, X. Huang, J. Yang and JAK. Suykens),
*IEEE Transactions on Neural Networks and Learning Systems***31**(2020),2965-2979. - Sparse SIR: optimal rates and adaptive estimation (with K. Tan and Z. Yu),
*Annals of Statistics***48**(2020), 64-85. - Sparse kernel regression with coefficient-based lq-regularization (with X. Huang, Y. Feng and JAK. Suykens),
*Journal of Machine Learning Research***20**(2019), 1-44. - Fast and strong convergence of online learning algorithms (with Z.-C. Guo),
*Advances in Computational Mathematics***45**(2019), 2745-2770. - Distributed learning with indefinite kernels,
*Analysis and Applications***17**(2019), 947-975. - Optimal rates for coefficient-based regularized regression (with Z.-C. Guo),
*Applied and Computational Harmonic Analysis***47**(2019), 662-701. - An RKHS approach to estimate individualized treatment rules based on functional predictors (with J. Fan and F. Lv),
*Mathematical Foundations of Computing***2**(2019), 169-181. - Nyström subsampling method for coefficient-based regularized regression (with L. Ma and Z. Wu),
*Inverse Problems***35**(2019), 075002 (40pp). - Robust mixed one-bit compressive sensing (with X. Huang, Y. Huang, Y. Huang, F. He, A. Maier, and M. Yan),
*Signal Processing***162**(2019), 161-168. - Distributed learning with multi-penalty regularization (with Z.-C. Guo and S.-B. Lin),
*Applied and Computational Harmonic Analysis***46**(2019), 478-499. - Gradient descent for robust kernel-based regression (with Z.-C. Guo and T. Hu),
*Inverse Problems***34**(2018), 065009 (29pp). - Convergence of unregularized online learning algorithms (with Y. Lei and Z.-C. Guo),
*Journal of Machine Learning Research***18**(2018), 1-33. - Pinball loss minimization for one-bit compressive sensing (with X. Huang, M. Yan and JAK. Suykens),
*Neurocomputing***314**(2018), 275-283. - Learning theory of distributed regression with bias corrected regularization kernel network (with Z.-C. Guo and Q. Wu),
*Journal of Machine Learning Research***18**(2017), 1-25. - Solution path for pin-SVM classifiers with positive and negative tau value (with X. Huang and JAK Suykens),
*IEEE Transactions on Neural Networks and Learning Systems***28**(2017), 1584-1593. - Learning rates for regularized least squares ranking algorithm (with Y. Zhao and J. Fan),
*Analysis and Applications***15**(2017), 815-836. - Nonconvex sorted l1 minimization for sparse approximation (with X. Huang and M. Yan),
*Journal of the Operations Research Society of China***3**(2015), 207-229. - Learning with the maximum correntropy criterion induced losses for regression (with Y. Feng, X. Huang, Y. Yang and JAK. Suykens)，
*Journal of Machine Learning Research***16**(2015), 993-1034. - Two-level l1 minimization for compressed sensing (with X. Huang, Y. Liu, S. Van Huffel and JAK. Suykens),
*Signal Processing***108**(2015), 205204, 459-475. - Sequential minimal optimization for SVM with pinball loss (with X. Huang and JAK. Suykens),
*Neurocomputing***149**(2015), 1596-1603. - Ramp loss linear programming support vector machine (with X. Huang and JAK. Suykens),
*Journal of Machine Learning Research***15**(2014), 2185-2211. - Support vector machine classifier with pinball loss (with X. Huang and JAK. Suykens),
*IEEE Transactions on Pattern Analysis and Machine Intelligence***36**(2014), 984-997. - Quantile regression with l1-regularization and Gaussian kernels (with X. Huang, Z. Tian and JAK. Suykens)，
*Advances in Computational Mathematics***40**(2014), 517-551. - Asymmetric ν-tube support vector regression (with X. Huang, K. Pelckmansand and JAK. Suykens),
*Computational Statistics & Data Analysis***77**(2014), 371-382. - Asymmetric least squares support vector machine classifiers (with X. Huang and JAK. Suykens),
*Computational Statistics & Data Analysis***70**(2014), 395-405. - Learning with coefficient-based regularization and l1-penalty (with Z.-C. Guo),
*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. - Non-uniform randomized sampling for multivariate approximation by high order Parzen windows (with X.-J. Zhou and D.-X. Zhou),
*Canadian Mathematical Bulletin***54**(2011), 566-576. - Classification with non-iid sampling (with Z.-C. Guo),
*Mathematical and Computer Modelling***54**(2011), 1347-1364. - Concentration estimates for learning with l1-regularizer and data dependent hypothesis spaces (with Y. Feng and D.-X. Zhou),
*Applied and Computational Harmonic Analysis***31**(2011), 286-302. - Normal estimation on manifolds by gradient learning (with D.-X. Zhou),
*Numerical Linear Algebra with Applications***18**(2011), 249-259. - Learning theory viewpoint of approximation by positive linear operators (with S. Lv),
*Computers and Mathematics with Applications***60**(2010), 3177-3186. - Hermite learning with gradient data (with X. Guo and D.-X. Zhou),
*Journal of Computational and Applied Mathematics***233**(2010), 3046-3059.