Showing math.NAShow all
3 papers · 1 filter
math.NA2024
Adaptive and non-adaptive randomized approximation of high-dimensional vectors
Robert J. Kunsch, Marcin Wnuk
We study approximation of the embedding , , based on randomized algorithms that use up to arbitrary linear function…
math.NA2024
Data Compression using Rank-1 Lattices for Parameter Estimation in Machine Learning
Michael Gnewuch, Kumar Harsha, Marcin Wnuk
The mean squared error and regularized versions of it are standard loss functions in supervised machine learning. However, calculating these losses for large data sets can be compu…
math.NA2019
On Negatively Dependent Sampling Schemes, Variance Reduction, and Probabilistic Upper Discrepancy Bounds
Michael Gnewuch, Marcin Wnuk, Nils Hebbinghaus
We study some notions of negative dependence of a sampling scheme that can be used to derive variance bounds for the corresponding estimator or discrepancy bounds for the underlyin…