Web: http://arxiv.org/abs/2108.12515

June 23, 2022, 1:12 a.m. | Maarten V. de Hoop, Nikola B. Kovachki, Nicholas H. Nelsen, Andrew M. Stuart

stat.ML updates on arXiv.org arxiv.org

This paper studies the learning of linear operators between
infinite-dimensional Hilbert spaces. The training data comprises pairs of
random input vectors in a Hilbert space and their noisy images under an unknown
self-adjoint linear operator. Assuming that the operator is diagonalizable in a
known basis, this work solves the equivalent inverse problem of estimating the
operator's eigenvalues given the data. Adopting a Bayesian approach, the
theoretical analysis establishes posterior contraction rates in the infinite
data limit with Gaussian priors that …

arxiv convergence data learning linear math operators

More from arxiv.org / stat.ML updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY