{"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/automatic-differentiation-variational","title":"Automatic Differentiation Variational Inference","arxiv_id":"1603.00788","date":"2016-03-02","proceeding":null,"authors":["Alp Kucukelbir","Dustin Tran","Rajesh Ranganath","Andrew Gelman","David M. Blei"],"abstract":"Probabilistic modeling is iterative. A scientist posits a simple model, fits\nit to her data, refines it according to her analysis, and repeats. However,\nfitting complex models to large data is a bottleneck in this process. Deriving\nalgorithms for new models can be both mathematically and computationally\nchallenging, which makes it difficult to efficiently cycle through the steps.\nTo this end, we develop automatic differentiation variational inference (ADVI).\nUsing our method, the scientist only provides a probabilistic model and a\ndataset, nothing else. ADVI automatically derives an efficient variational\ninference algorithm, freeing the scientist to refine and explore many models.\nADVI supports a broad class of models-no conjugacy assumptions are required. We\nstudy ADVI across ten different models and apply it to a dataset with millions\nof observations. ADVI is integrated into Stan, a probabilistic programming\nsystem; it is available for immediate use.","url_abs":"http://arxiv.org/abs/1603.00788v1","url_pdf":"http://arxiv.org/pdf/1603.00788v1.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":"automatic-differentiation-variational","repo_url":"https://github.com/JulienNonin/auto-diff-variational-inference","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"automatic-differentiation-variational","repo_url":"https://github.com/TuringLang/Bijectors.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"automatic-differentiation-variational","repo_url":"https://github.com/UnofficialJuliaMirrorSnapshots/Bijectors.jl-76274a88-744f-5084-9051-94815aaf08c4","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"automatic-differentiation-variational","repo_url":"https://github.com/modichirag/gsm-vi","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"jax","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"probabilistic-programming","task_name":"Probabilistic Programming"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.00788","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}