{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-easy-rejection-sampling-baseline-via","title":"An Easy Rejection Sampling Baseline via Gradient Refined Proposals","arxiv_id":"2310.00300","date":"2023-09-30","proceeding":null,"authors":["Edward Raff","Mark McLean","James Holt"],"abstract":"Rejection sampling is a common tool for low dimensional problems ($d \\leq 2$), often touted as an \"easy\" way to obtain valid samples from a distribution $f(\\cdot)$ of interest. In practice it is non-trivial to apply, often requiring considerable mathematical effort to devise a good proposal distribution $g(\\cdot)$ and select a supremum $C$. More advanced samplers require additional mathematical derivations, limitations on $f(\\cdot)$, 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 $f(\\cdot)$ 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 $7.3\\times$, while requiring no extra assumptions or any derivations to use: only a differentiable $f(\\cdot)$ 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.","url_abs":"https://arxiv.org/abs/2310.00300v1","url_pdf":"https://arxiv.org/pdf/2310.00300v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"an-easy-rejection-sampling-baseline-via","repo_url":"https://github.com/NeuromorphicComputationResearchProgram/EasyRejectionSampling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"jax","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}