{"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/implicit-maximum-likelihood-estimation","title":"Implicit Maximum Likelihood Estimation","arxiv_id":"1809.09087","date":"2018-09-24","proceeding":"ICLR 2019 5","authors":["Ke Li","Jitendra Malik"],"abstract":"Implicit probabilistic models are models defined naturally in terms of a\nsampling procedure and often induces a likelihood function that cannot be\nexpressed explicitly. We develop a simple method for estimating parameters in\nimplicit models that does not require knowledge of the form of the likelihood\nfunction or any derived quantities, but can be shown to be equivalent to\nmaximizing likelihood under some conditions. Our result holds in the\nnon-asymptotic parametric setting, where both the capacity of the model and the\nnumber of data examples are finite. We also demonstrate encouraging\nexperimental results.","url_abs":"http://arxiv.org/abs/1809.09087v2","url_pdf":"http://arxiv.org/pdf/1809.09087v2.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":"abstracts"},"code_links":[{"paper_slug":"implicit-maximum-likelihood-estimation","repo_url":"https://github.com/yedidh/glann","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.09087","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}