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Gradient-based Optimization for Regression in the Functional
  Tensor-Train Format

Gradient-based Optimization for Regression in the Functional Tensor-Train Format

3 January 2018
Alex A. Gorodetsky
J. Jakeman
ArXivPDFHTML

Papers citing "Gradient-based Optimization for Regression in the Functional Tensor-Train Format"

3 / 3 papers shown
Title
Control Variate Polynomial Chaos: Optimal Fusion of Sampling and
  Surrogates for Multifidelity Uncertainty Quantification
Control Variate Polynomial Chaos: Optimal Fusion of Sampling and Surrogates for Multifidelity Uncertainty Quantification
Hang Yang
Y. Fujii
K. W. Wang
Alex A. Gorodetsky
40
6
0
26 Jan 2022
Mercer kernels and integrated variance experimental design: connections
  between Gaussian process regression and polynomial approximation
Mercer kernels and integrated variance experimental design: connections between Gaussian process regression and polynomial approximation
Alex A. Gorodetsky
Youssef M. Marzouk
36
38
0
27 Feb 2015
High-dimensional additive modeling
High-dimensional additive modeling
L. Meier
Sara van de Geer
Peter Buhlmann
201
481
0
25 Jun 2008
1