all AI news
Local-Global MCMC kernels: the best of both worlds. (arXiv:2111.02702v3 [stat.ML] UPDATED)
Oct. 5, 2022, 1:12 a.m. | Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines
cs.LG updates on arXiv.org arxiv.org
Recent works leveraging learning to enhance sampling have shown promising
results, in particular by designing effective non-local moves and global
proposals. However, learning accuracy is inevitably limited in regions where
little data is available such as in the tails of distributions as well as in
high-dimensional problems. In the present paper we study an Explore-Exploit
Markov chain Monte Carlo strategy ($Ex^2MCMC$) that combines local and global
samplers showing that it enjoys the advantages of both approaches. We prove
$V$-uniform geometric …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
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
Consultant - Artificial Intelligence & Data (Google Cloud Data Engineer) - MY / TH
@ Deloitte | Kuala Lumpur, MY