May 27, 2024, 4:44 a.m. | Isaac Reid, Eli Berger, Krzysztof Choromanski, Adrian Weller

cs.LG updates on arXiv.org arxiv.org

arXiv:2310.04854v2 Announce Type: replace-cross
Abstract: We present a novel quasi-Monte Carlo mechanism to improve graph-based sampling, coined repelling random walks. By inducing correlations between the trajectories of an interacting ensemble such that their marginal transition probabilities are unmodified, we are able to explore the graph more efficiently, improving the concentration of statistical estimators whilst leaving them unbiased. The mechanism has a trivial drop-in implementation. We showcase the effectiveness of repelling random walks in a range of settings including estimation of …

abstract arxiv correlations cs.lg ensemble explore graph graph-based improving novel random replace sampling statistical stat.ml the graph them transition type unbiased

Senior Data Engineer

@ Displate | Warsaw

Principal Architect

@ eSimplicity | Silver Spring, MD, US

Embedded Software Engineer

@ Carrier | CAN03: Carrier-Charlotte, NC 9701 Old Statesville Road, Charlotte, NC, 28269 USA

(USA) Software Engineer III

@ Roswell Park Comprehensive Cancer Center | (USA) CA SUNNYVALE Home Office SUNNYVALE III - 840 W CALIFORNIA

Experienced Manufacturing and Automation Engineer

@ Boeing | DEU - Munich, Germany

Software Engineering-Sr Engineer (Java 17, Python, Microservices, Spring Boot, REST)

@ FICO | Bengaluru, India