{"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/black-box-variational-inference","title":"Black Box Variational Inference","arxiv_id":"1401.0118","date":"2013-12-31","proceeding":null,"authors":["Rajesh Ranganath","Sean Gerrish","David M. Blei"],"abstract":"Variational inference has become a widely used method to approximate\nposteriors in complex latent variables models. However, deriving a variational\ninference algorithm generally requires significant model-specific analysis, and\nthese efforts can hinder and deter us from quickly developing and exploring a\nvariety of models for a problem at hand. In this paper, we present a \"black\nbox\" variational inference algorithm, one that can be quickly applied to many\nmodels with little additional derivation. Our method is based on a stochastic\noptimization of the variational objective where the noisy gradient is computed\nfrom Monte Carlo samples from the variational distribution. We develop a number\nof methods to reduce the variance of the gradient, always maintaining the\ncriterion that we want to avoid difficult model-based derivations. We evaluate\nour method against the corresponding black box sampling based methods. We find\nthat our method reaches better predictive likelihoods much faster than sampling\nmethods. Finally, we demonstrate that Black Box Variational Inference lets us\neasily explore a wide space of models by quickly constructing and evaluating\nseveral models of longitudinal healthcare data.","url_abs":"http://arxiv.org/abs/1401.0118v1","url_pdf":"http://arxiv.org/pdf/1401.0118v1.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":"black-box-variational-inference","repo_url":"https://github.com/artiste-qb-net/Quantum_Edward","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"black-box-variational-inference","repo_url":"https://github.com/jamesvuc/BBVI","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"stochastic-optimization","task_name":"Stochastic Optimization"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1401.0118","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1401.0118"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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