{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:41:37Z","timestamp":1773816097871,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>We present a novel Metropolis-Hastings method for large datasets that uses small expected-size mini-batches of data. Previous work on reducing the cost of Metropolis-Hastings tests yields only constant factor reductions versus using the full dataset for each sample. Here we present a method that can be tuned to provide arbitrarily small batch sizes, by adjusting either proposal step size or temperature. Our test uses the noise-tolerant Barker acceptance test with a novel additive correction variable. The resulting test has similar cost to a normal SGD update. Our experiments demonstrate several order-of-magnitude speedups over previous work.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/753","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"5359-5363","source":"Crossref","is-referenced-by-count":8,"title":["An Efficient Minibatch Acceptance Test for Metropolis-Hastings"],"prefix":"10.24963","author":[{"given":"Daniel","family":"Seita","sequence":"first","affiliation":[{"name":"University of California, Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinlei","family":"Pan","sequence":"additional","affiliation":[{"name":"University of California, Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"University of California, Berkeley"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Canny","sequence":"additional","affiliation":[{"name":"University of California, Berkeley"},{"name":"Google Research"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:55:44Z","timestamp":1530755744000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/753"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/753","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}