{"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/augment-and-reduce-stochastic-inference-for-1","title":"Augment and Reduce: Stochastic Inference for Large Categorical Distributions","arxiv_id":"1802.04220","date":"2018-02-12","proceeding":"ICML 2018","authors":["Francisco J. R. Ruiz","Michalis K. Titsias","Adji B. Dieng","David M. Blei"],"abstract":"Categorical distributions are ubiquitous in machine learning, e.g., in\nclassification, language models, and recommendation systems. However, when the\nnumber of possible outcomes is very large, using categorical distributions\nbecomes computationally expensive, as the complexity scales linearly with the\nnumber of outcomes. To address this problem, we propose augment and reduce\n(A&R), a method to alleviate the computational complexity. A&R uses two ideas:\nlatent variable augmentation and stochastic variational inference. It maximizes\na lower bound on the marginal likelihood of the data. Unlike existing methods\nwhich are specific to softmax, A&R is more general and is amenable to other\ncategorical models, such as multinomial probit. On several large-scale\nclassification problems, we show that A&R provides a tighter bound on the\nmarginal likelihood and has better predictive performance than existing\napproaches.","url_abs":"http://arxiv.org/abs/1802.04220v3","url_pdf":"http://arxiv.org/pdf/1802.04220v3.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":"augment-and-reduce-stochastic-inference-for-1","repo_url":"https://github.com/franrruiz/augment-reduce","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.04220","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}