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math.NA2026
The devil in the (de)tails: an improved recovery guarantee for sparse approximation
Ben Adcock, Simone Brugiapaglia, Avi Gupta
Many functions exhibit approximate sparsity in their coefficients with respect to a given dictionary. In recent literature, sparse approximation in such a dictionary from i.i.d. po…
math.NA2026
Universal, sample-optimal algorithms for recovery of anisotropic functions from i.i.d. samples
Ben Adcock, Avi Gupta
A key problem in approximation theory is the recovery of high-dimensional functions from samples. In many cases, the functions of interest exhibit anisotropic smoothness, and, in m…
math.NA2026
Christoffel Adaptive Sampling for Sparse Random Feature Expansions
Ben Adcock, Khiem Can, Xuemeng Wang
Random Feature Models (RFMs) have become a powerful tool for approximating multivariate functions and solving partial differential equations efficiently. Sparse Random Feature Expa…