{"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-structured-variational-inference","title":"Automatic structured variational inference","arxiv_id":"2002.00643","date":"2020-02-03","proceeding":null,"authors":["Luca Ambrogioni","Kate Lin","Emily Fertig","Sharad Vikram","Max Hinne","Dave Moore","Marcel van Gerven"],"abstract":"Stochastic variational inference offers an attractive option as a default method for differentiable probabilistic programming. However, the performance of the variational approach depends on the choice of an appropriate variational family. Here, we introduce automatic structured variational inference (ASVI), a fully automated method for constructing structured variational families, inspired by the closed-form update in conjugate Bayesian models. These convex-update families incorporate the forward pass of the input probabilistic program and can therefore capture complex statistical dependencies. Convex-update families have the same space and time complexity as the input probabilistic program and are therefore tractable for a very large family of models including both continuous and discrete variables. We validate our automatic variational method on a wide range of low- and high-dimensional inference problems. We find that ASVI provides a clear improvement in performance when compared with other popular approaches such as the mean-field approach and inverse autoregressive flows. We provide an open source implementation of ASVI in TensorFlow Probability.","url_abs":"https://arxiv.org/abs/2002.00643v3","url_pdf":"https://arxiv.org/pdf/2002.00643v3.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-structured-variational-inference","repo_url":"https://github.com/google-research/google-research/tree/master/automatic_structured_vi","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"automatic-structured-variational-inference","repo_url":"https://github.com/GianluigiSilvestri/asvi","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"probabilistic-programming","task_name":"Probabilistic Programming"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[{"method_slug":"asvi","method_name":"ASVI"}],"datasets_introduced":[],"methods_introduced":[{"slug":"asvi","name":"ASVI","full_name":"Automatic Structured Variational Inference"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2002.00643","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}