Lei Shi

J. Approx. Theory, January, 2024

2023

Learning With Asymmetric Kernels: Least Squares and Feature Interpretation.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., August, 2023

Enhancing Kernel Flexibility via Learning Asymmetric Locally-Adaptive Kernels.

[BibT_eX]

[DOI]

CoRR, 2023

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Classification with Deep Neural Networks and Logistic Loss.

[BibT_eX]

[DOI]

Zihan Zhang

Ding-Xuan Zhou

CoRR, 2023

Optimality of Robust Online Learning.

[BibT_eX]

[DOI]

Andreas Christmann

CoRR, 2023

2022

Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2022

A Decentralized Framework for Kernel PCA with Projection Consensus Constraints.

[BibT_eX]

[DOI]

CoRR, 2022

Capacity dependent analysis for functional online learning algorithms.

[BibT_eX]

[DOI]

Xin Guo

CoRR, 2022

Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Analysis of regularized least-squares in reproducing kernel Kreĭn spaces.

[BibT_eX]

[DOI]

Mach. Learn., 2021

Generalization Properties of hyper-RKHS and its Applications.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2021

Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs.

[BibT_eX]

[DOI]

CoRR, 2021

2020

A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2020

Realizing Data Features by Deep Nets.

[BibT_eX]

[DOI]

Shao-Bo Lin

IEEE Trans. Neural Networks Learn. Syst., 2020

Preface of the special issue on analysis in data science: Methods and applications.

[BibT_eX]

[DOI]

Xin Guo

Math. Found. Comput., 2020

Fast algorithms for robust principal component analysis with an upper bound on the rank.

[BibT_eX]

[DOI]

Ningyu Sha

Ming Yan

CoRR, 2020

Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Robust mixed one-bit compressive sensing.

[BibT_eX]

[DOI]

Signal Process., 2019

Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2019

Fast and strong convergence of online learning algorithms.

[BibT_eX]

[DOI]

Adv. Comput. Math., 2019

2018

Pinball loss minimization for one-bit compressive sensing: Convex models and algorithms.

[BibT_eX]

[DOI]

Neurocomputing, 2018

Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions.

[BibT_eX]

[DOI]

CoRR, 2018

2017

Solution Path for Pin-SVM Classifiers With Positive and Negative τ Values.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2017

Convergence of Unregularized Online Learning Algorithms.

[BibT_eX]

[DOI]

Yunwen Lei

J. Mach. Learn. Res., 2017

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network.

[BibT_eX]

[DOI]

Qiang Wu

J. Mach. Learn. Res., 2017

Mixed one-bit compressive sensing with applications to overexposure correction for CT reconstruction.

[BibT_eX]

[DOI]

CoRR, 2017

2015

Two-level ℓ<sub>1</sub> minimization for compressed sensing.

[BibT_eX]

[DOI]

Signal Process., 2015

Learning with the maximum correntropy criterion induced losses for regression.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2015

Sequential minimal optimization for SVM with pinball loss.

[BibT_eX]

[DOI]

Neurocomputing, 2015

Pinball Loss Minimization for One-bit Compressive Sensing.

[BibT_eX]

[DOI]

CoRR, 2015

2014

Support Vector Machine Classifier With Pinball Loss.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2014

Ramp loss linear programming support vector machine.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2014

Asymmetric least squares support vector machine classifiers.

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2014

Asymmetric v-tube support vector regression.

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2014

Quantile regression with ℓ 1 - regularization and Gaussian kernels.

[BibT_eX]

[DOI]

Adv. Comput. Math., 2014

2013

Learning with coefficient-based regularization and ℓ1 -penalty.

[BibT_eX]

[DOI]

Adv. Comput. Math., 2013

2011

Normal estimation on manifolds by gradient learning.

[BibT_eX]

[DOI]

Ding-Xuan Zhou

Numer. Linear Algebra Appl., 2011

Classification with non-i.i.d. sampling.

[BibT_eX]

[DOI]

Math. Comput. Model., 2011

2010

Hermite learning with gradient data.

[BibT_eX]

[DOI]

Xin Guo

Ding-Xuan Zhou

J. Comput. Appl. Math., 2010

Learning theory viewpoint of approximation by positive linear operators.

[BibT_eX]

[DOI]

Shaogao Lv

Comput. Math. Appl., 2010

2009

Gradient Learning Approach for Variable Selection in Credit Scoring.

[BibT_eX]

[DOI]

Quan-Wu Xiao