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Improving sample and feature selection with principal covariates regression

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Improving sample and feature selection with principal covariates regression

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Structure-property maps with Kernel principal covariates regression

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Automatic L2 Regularization for Multiple Generalized Additive Models

Hybrid regularisation and the (in)admissibility of ridge regression in infinite dimensional Hilbert spaces

MATHICSE Technical Report: A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Methodology And Convergence Rates For Functional Time Series Regression

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Structure-property maps with Kernel principal covariates regression

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Automatic L2 Regularization for Multiple Generalized Additive Models

Hybrid regularisation and the (in)admissibility of ridge regression in infinite dimensional Hilbert spaces

MATHICSE Technical Report: A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Methodology And Convergence Rates For Functional Time Series Regression

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression