4 citations · 7 across the 10 of their papers we have counts for
4 papers · 1 filter
An approximation theory for Markov chain compression
Mark Fornace, Michael Lindsey
We develop a framework for the compression of reversible Markov chains with rigorous error control. Given a subset of selected states, we construct reduced dynamics that can be lif…
MNE: overparametrized neural evolution with applications to diffusion processes and sampling
Michael Lindsey
We propose a framework for solving evolution equations within parametric function classes, especially ones that are specified by neural networks. We call this framework the minimal…
Column and row subset selection using nuclear scores: algorithms and theory for Nyström approximation, CUR decomposition, and graph Laplacian reduction
Mark Fornace, Michael Lindsey
Column selection is an essential tool for structure-preserving low-rank approximation, with wide-ranging applications across many fields, such as data science, machine learning, an…
Multiscale interpolative construction of quantized tensor trains
Michael Lindsey
Quantized tensor trains (QTTs) have recently emerged as a framework for the numerical discretization of continuous functions, with the potential for widespread applications in nume…