March 12, 2024, 4:45 a.m. | Nisha Chandramoorthy, Florian Schaefer, Youssef Marzouk

cs.LG updates on arXiv.org arxiv.org

arXiv:2305.09792v3 Announce Type: replace-cross
Abstract: We propose a new approach for sampling and Bayesian computation that uses the score of the target distribution to construct a transport from a given reference distribution to the target. Our approach is an infinite-dimensional Newton method, involving a linear PDE, for finding a zero of a ``score-residual'' operator. We prove sufficient conditions for convergence to a valid transport map. Our Newton iterates can be computed by exploiting fast solvers for elliptic PDEs, resulting in …

abstract arxiv bayesian computation construct cs.lg cs.na distribution linear math.na math.st prove reference residual sampling stat.th transport type

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne