{"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/a-practical-algorithm-for-topic-modeling-with","title":"A Practical Algorithm for Topic Modeling with Provable Guarantees","arxiv_id":"1212.4777","date":"2012-12-19","proceeding":null,"authors":["Sanjeev Arora","Rong Ge","Yoni Halpern","David Mimno","Ankur Moitra","David Sontag","Yichen Wu","Michael Zhu"],"abstract":"Topic models provide a useful method for dimensionality reduction and\nexploratory data analysis in large text corpora. Most approaches to topic model\ninference have been based on a maximum likelihood objective. Efficient\nalgorithms exist that approximate this objective, but they have no provable\nguarantees. Recently, algorithms have been introduced that provide provable\nbounds, but these algorithms are not practical because they are inefficient and\nnot robust to violations of model assumptions. In this paper we present an\nalgorithm for topic model inference that is both provable and practical. The\nalgorithm produces results comparable to the best MCMC implementations while\nrunning orders of magnitude faster.","url_abs":"http://arxiv.org/abs/1212.4777v1","url_pdf":"http://arxiv.org/pdf/1212.4777v1.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":"a-practical-algorithm-for-topic-modeling-with","repo_url":"https://github.com/moontae/jsmf-raw","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"a-practical-algorithm-for-topic-modeling-with","repo_url":"https://github.com/sc782/pyJSMF-RAW","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"topic-models","task_name":"Topic Models"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1212.4777","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}