2025
Debiased transfer learning estimation and inference for multinomial regression.
Stat. Comput., June, 2025
Sparse and debiased Lasso estimation and statistical inference for long time series via divide-and-conquer.
Stat. Comput., June, 2025
Optimal distributed subsampling under heterogeneity.
Stat. Comput., April, 2025
Kernel-Based Regularized Learning with Random Projections: Beyond Least Squares.
SIAM J. Math. Data Sci., 2025
Improved analysis of supervised learning in the RKHS with random features: Beyond least squares.
Neural Networks, 2025
Optimal distributed subsampling for expected shortfall regression via Neyman-orthogonal score.
Knowl. Based Syst., 2025
Optimal decorrelated score subsampling for Cox regression with massive survival data.
Neurocomputing, 2025
Metric-Agnostic Continual Learning for Sustainable Group Fairness.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025
2024
Distributed Estimation of Support Vector Machines for Matrix Data.
IEEE Trans. Neural Networks Learn. Syst., May, 2024
Image classification based on tensor network DenseNet model.
Appl. Intell., April, 2024
Statistical performance of quantile tensor regression with convex regularization.
J. Multivar. Anal., March, 2024
Online Learning From Evolving Feature Spaces With Deep Variational Models.
IEEE Trans. Knowl. Data Eng., 2024
Distributed statistical estimation in quantile regression over a network.
Signal Process., 2024
Robust low tubal rank tensor recovery via L2E criterion.
Pattern Recognit., 2024
More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization.
J. Mach. Learn. Res., 2024
Adaptive Huber trace regression with low-rank matrix parameter via nonconvex regularization.
J. Complex., 2024
Linear convergence of decentralized estimation for statistical estimation using gradient method.
Neurocomputing, 2024
HGDL: Heterogeneous Graph Label Distribution Learning.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Utilitarian Online Learning from Open-World Soft Sensing.
Proceedings of the IEEE International Conference on Data Mining, 2024
Urban Functional Zone Delineation Aided by Vision Foundation Models.
Proceedings of the IEEE International Conference on Data Mining, 2024
2023
On Optimal Learning With Random Features.
IEEE Trans. Neural Networks Learn. Syst., November, 2023
Communication-efficient estimation of quantile matrix regression for massive datasets.
Comput. Stat. Data Anal., November, 2023
Value iteration for streaming data on a continuous space with gradient method in an RKHS.
Neural Networks, September, 2023
Properties of Standard and Sketched Kernel Fisher Discriminant.
IEEE Trans. Pattern Anal. Mach. Intell., August, 2023
Semiparametric function-on-function quantile regression model with dynamic single-index interactions.
Comput. Stat. Data Anal., June, 2023
Image recognition and classification with HOG based on nonlinear support tensor machine.
Multim. Tools Appl., May, 2023
Best subset selection for high-dimensional non-smooth models using iterative hard thresholding.
Inf. Sci., May, 2023
On Linear Convergence of ADMM for Decentralized Quantile Regression.
IEEE Trans. Signal Process., 2023
Functional additive expectile regression in the reproducing kernel Hilbert space.
J. Multivar. Anal., 2023
Semiparametric penalized quadratic inference functions for longitudinal data in ultra-high dimensions.
J. Multivar. Anal., 2023
2022
Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification.
J. Mach. Learn. Res., 2022
Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions.
J. Mach. Learn. Res., 2022
Life History Recorded in the Vagino-cervical Microbiome Along with Multi-omes.
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Genom. Proteom. Bioinform., 2022
Sparse high-dimensional semi-nonparametric quantile regression in a reproducing kernel Hilbert space.
Comput. Stat. Data Anal., 2022
Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Online Deep Learning from Doubly-Streaming Data.
Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022
2021
Distributed Partially Linear Additive Models With a High Dimensional Linear Part.
IEEE Trans. Signal Inf. Process. over Networks, 2021
Learning Rate for Convex Support Tensor Machines.
IEEE Trans. Neural Networks Learn. Syst., 2021
Distributed learning for sketched kernel regression.
Neural Networks, 2021
Optimal prediction for high-dimensional functional quantile regression in reproducing kernel Hilbert spaces.
J. Complex., 2021
Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection.
Inf. Sci., 2021
Sketched quantile additive functional regression.
Neurocomputing, 2021
2020
Debiasing and Distributed Estimation for High-Dimensional Quantile Regression.
IEEE Trans. Neural Networks Learn. Syst., 2020
Randomized sketches for kernel CCA.
Neural Networks, 2020
Nonlinear functional canonical correlation analysis via distance covariance.
J. Multivar. Anal., 2020
Randomized sketches for sparse additive models.
Neurocomputing, 2020
A reproducing kernel Hilbert space approach to high dimensional partially varying coefficient model.
Comput. Stat. Data Anal., 2020
Partially functional linear regression in reproducing kernel Hilbert spaces.
Comput. Stat. Data Anal., 2020
2019
Estimation and testing for partially functional linear errors-in-variables models.
J. Multivar. Anal., 2019
Rank reduction for high-dimensional generalized additive models.
J. Multivar. Anal., 2019
Reduced rank modeling for functional regression with functional responses.
J. Multivar. Anal., 2019
Regression adjustment for treatment effect with multicollinearity in high dimensions.
Comput. Stat. Data Anal., 2019
Estimation for single-index models via martingale difference divergence.
Comput. Stat. Data Anal., 2019
Pursuit of dynamic structure in quantile additive models with longitudinal data.
Comput. Stat. Data Anal., 2019
2018
On the sign consistency of the Lasso for the high-dimensional Cox model.
J. Multivar. Anal., 2018
Quantile regression for additive coefficient models in high dimensions.
J. Multivar. Anal., 2018
Time-varying quantile single-index model for multivariate responses.
Comput. Stat. Data Anal., 2018
A principal varying-coefficient model for quantile regression: Joint variable selection and dimension reduction.
Comput. Stat. Data Anal., 2018
Estimation and testing for time-varying quantile single-index models with longitudinal data.
Comput. Stat. Data Anal., 2018
2017
Quantile index coefficient model with variable selection.
J. Multivar. Anal., 2017
Estimation and variable selection for quantile partially linear single-index models.
J. Multivar. Anal., 2017
Estimation and model identification of longitudinal data time-varying nonparametric models.
J. Multivar. Anal., 2017
Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models.
J. Multivar. Anal., 2017
Divide-and-Conquer for Debiased $l_1$-norm Support Vector Machine in Ultra-high Dimensions.
J. Mach. Learn. Res., 2017
Composite quantile regression for correlated data.
Comput. Stat. Data Anal., 2017
2016
Mean and quantile boosting for partially linear additive models.
Stat. Comput., 2016
Separation of linear and index covariates in partially linear single-index models.
J. Multivar. Anal., 2016
Nonconvex penalized reduced rank regression and its oracle properties in high dimensions.
J. Multivar. Anal., 2016
Posterior convergence for Bayesian functional linear regression.
J. Multivar. Anal., 2016
Minimax convergence rates for kernel CCA.
J. Multivar. Anal., 2016
Estimation and variable selection for proportional response data with partially linear single-index models.
Comput. Stat. Data Anal., 2016
The Expectation-Maximization approach for Bayesian quantile regression.
Comput. Stat. Data Anal., 2016
Robust closed-form estimators for the integer-valued GARCH (1, 1) model.
Comput. Stat. Data Anal., 2016
Reduced rank regression with possibly non-smooth criterion functions: An empirical likelihood approach.
Comput. Stat. Data Anal., 2016
A new nested Cholesky decomposition and estimation for the covariance matrix of bivariate longitudinal data.
Comput. Stat. Data Anal., 2016
2015
Variable selection and estimation for partially linear single-index models with longitudinal data.
Stat. Comput., 2015
Bayesian quantile regression for partially linear additive models.
Stat. Comput., 2015
Spline estimator for simultaneous variable selection and constant coefficient identification in high-dimensional generalized varying-coefficient models.
J. Multivar. Anal., 2015
Simultaneous estimation of linear conditional quantiles with penalized splines.
J. Multivar. Anal., 2015
Parametric and semiparametric reduced-rank regression with flexible sparsity.
J. Multivar. Anal., 2015
Quantile regression for dynamic partially linear varying coefficient time series models.
J. Multivar. Anal., 2015
Minimax prediction for functional linear regression with functional responses in reproducing kernel Hilbert spaces.
J. Multivar. Anal., 2015
A Note on Application of Nesterov's Method in Solving Lasso-Type Problems.
Commun. Stat. Simul. Comput., 2015
2014
Empirical likelihood inference for general transformation models with right censored data.
Stat. Comput., 2014
SCAD-penalized regression in additive partially linear proportional hazards models with an ultra-high-dimensional linear part.
J. Multivar. Anal., 2014
Series expansion for functional sufficient dimension reduction.
J. Multivar. Anal., 2014
Semiparametric Bayesian information criterion for model selection in ultra-high dimensional additive models.
J. Multivar. Anal., 2014
Variational inferences for partially linear additive models with variable selection.
Comput. Stat. Data Anal., 2014
Partially linear structure identification in generalized additive models with NP-dimensionality.
Comput. Stat. Data Anal., 2014
2013
Bayesian quantile regression for single-index models.
Stat. Comput., 2013
Empirical likelihood for partially linear proportional hazards models with growing dimensions.
J. Multivar. Anal., 2013
Quadratic inference functions for partially linear single-index models with longitudinal data.
J. Multivar. Anal., 2013
Sparse-smooth regularized singular value decomposition.
J. Multivar. Anal., 2013
A simple and efficient algorithm for fused lasso signal approximator with convex loss function.
Comput. Stat., 2013
Shrinkage variable selection and estimation in proportional hazards models with additive structure and high dimensionality.
Comput. Stat. Data Anal., 2013
Automatic variable selection for longitudinal generalized linear models.
Comput. Stat. Data Anal., 2013
2012
Gaussian Process Single-Index Models as Emulators for Computer Experiments.
Technometrics, 2012
On feature selection with principal component analysis for one-class SVM.
Pattern Recognit. Lett., 2012
Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data.
J. Multivar. Anal., 2012
Time-varying coefficient estimation in differential equation models with noisy time-varying covariates.
J. Multivar. Anal., 2012
BOPA: A Bayesian hierarchical model for outlier expression detection.
Comput. Stat. Data Anal., 2012
2011
Semi-varying coefficient models with a diverging number of components.
J. Multivar. Anal., 2011
2010
Total variation, adaptive total variation and nonconvex smoothly clipped absolute deviation penalty for denoising blocky images.
Pattern Recognit., 2010
Sparse Bayesian hierarchical modeling of high-dimensional clustering problems.
J. Multivar. Anal., 2010
2009
Bayesian Nonlinear Principal Component Analysis Using Random Fields.
IEEE Trans. Pattern Anal. Mach. Intell., 2009
2008
Automated mapping of large-scale chromatin structure in ENCODE.
Bioinform., 2008
2007
On the Consistency of Bayesian Function Approximation Using Step Functions.
Neural Comput., 2007
2006
Variational Local Structure Estimation for Image Super-Resolution.
Proceedings of the International Conference on Image Processing, 2006