{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T23:03:32Z","timestamp":1775084612013,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,28]]},"abstract":"<jats:p>Rejection sampling is a common tool for low dimensional problems (d \u2264 2), often touted as an \u201ceasy\u201d way to obtain valid samples from a distribution f(\u00b7) of interest. In practice it is non-trivial to apply, often requiring considerable mathematical effort to devise a good proposal distribution g(\u00b7) and select a supremum C. More advanced samplers require additional mathematical derivations, limitations on f(\u00b7), or even cross-validation, making them difficult to apply. We devise a new approximate baseline approach to rejection sampling that works with less information, requiring only a differentiable f(\u00b7) be specified, making it easier to use. We propose a new approach to rejection sampling by refining a parameterized proposal distribution with a loss derived from the acceptance threshold. In this manner we obtain comparable or better acceptance rates on current benchmarks by up to 7.3\u00d7, while requiring no extra assumptions or any derivations to use: only a differentiable f(\u00b7) is required. While approximate, the results are correct with high probability, and in all tests pass a distributional check. This makes our approach easy to use, reproduce, and efficacious.<\/jats:p>","DOI":"10.3233\/faia230483","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:18:27Z","timestamp":1695979107000},"source":"Crossref","is-referenced-by-count":0,"title":["An Easy Rejection Sampling Baseline via Gradient Refined Proposals"],"prefix":"10.3233","author":[{"given":"Edward","family":"Raff","sequence":"first","affiliation":[{"name":"Laboratory for Physical Sciences"},{"name":"Booz Allen Hamilton"},{"name":"Syracuse University"},{"name":"University of Maryland, Baltimore County"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"McLean","sequence":"additional","affiliation":[{"name":"Laboratory for Physical Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Holt","sequence":"additional","affiliation":[{"name":"Laboratory for Physical Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230483","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:18:28Z","timestamp":1695979108000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230483","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}