High-dimensional functions with small effective dimension can be approximated by penalized least squares in anchored low-dimensional subspaces, with error bounds in Sobolev and mixed-regularity spaces.
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Constructive Approximation of High-Dimensional Functions with Small Efficient Dimension with Applications in Uncertainty Quantification
High-dimensional functions with small effective dimension can be approximated by penalized least squares in anchored low-dimensional subspaces, with error bounds in Sobolev and mixed-regularity spaces.