Spline interpolation on sparse grids

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Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality (2018, methodology): “In this paper, we give generalization error bounds of deep ReLU networks for a Besov space and its variant with mixed smoothness , which includes the H¨older space, the Sobolev space, and the function class with total variation as special cases.”
Hyperbolic Cross Approximation (2016, result): “We refer to [92, 93] and the recent papers [302, 396, 304, 49, 101, 111, 376].”
Generalized coorbit space theory and inhomogeneous function spaces of Besov-Lizorkin-Triebel type (2010, background): “ﬁcations for the B-spaces are according to Remark 5.11. 5.4 Spaces ofdominating mixed smoothness Recently, there has been an increasing interest in function spaces of dominating mixed smoothness, see [46, 2, 60, 57, 58, 47, 48]. Their structure is suitable for treating high-dimensional approximation”
On tensor products of quasi-Banach spaces (2010, background): “Moreover, counterparts of these results are known for spaces on [0, 1] and [0, 1](2), respectively, see [20].”