{"extern":"1","publication_status":"published","department":[{"_id":"FrLo"}],"quality_controlled":"1","external_id":{"arxiv":["1708.01733"]},"date_updated":"2023-09-13T07:52:40Z","alternative_title":["PMLR"],"title":"Boosting variational inference: An optimization perspective","citation":{"short":"F. Locatello, R. Khanna, J. Ghosh, G. Rätsch, in:, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, ML Research Press, 2018, pp. 464–472.","ista":"Locatello F, Khanna R, Ghosh J, Rätsch G. 2018. Boosting variational inference: An optimization perspective. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 84, 464–472.","apa":"Locatello, F., Khanna, R., Ghosh, J., & Rätsch, G. (2018). Boosting variational inference: An optimization perspective. In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (Vol. 84, pp. 464–472). Playa Blanca, Lanzarote: ML Research Press.","chicago":"Locatello, Francesco, Rajiv Khanna, Joydeep Ghosh, and Gunnar Rätsch. “Boosting Variational Inference: An Optimization Perspective.” In Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, 84:464–72. ML Research Press, 2018.","mla":"Locatello, Francesco, et al. “Boosting Variational Inference: An Optimization Perspective.” Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, vol. 84, ML Research Press, 2018, pp. 464–72.","ama":"Locatello F, Khanna R, Ghosh J, Rätsch G. Boosting variational inference: An optimization perspective. In: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. Vol 84. ML Research Press; 2018:464-472.","ieee":"F. Locatello, R. Khanna, J. Ghosh, and G. Rätsch, “Boosting variational inference: An optimization perspective,” in Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, Playa Blanca, Lanzarote, 2018, vol. 84, pp. 464–472."},"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1708.01733"}],"scopus_import":"1","oa_version":"Preprint","article_processing_charge":"No","day":"15","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","text":"Variational inference is a popular technique to approximate a possibly\r\nintractable Bayesian posterior with a more tractable one. Recently, boosting\r\nvariational inference has been proposed as a new paradigm to approximate the\r\nposterior by a mixture of densities by greedily adding components to the\r\nmixture. However, as is the case with many other variational inference\r\nalgorithms, its theoretical properties have not been studied. In the present\r\nwork, we study the convergence properties of this approach from a modern\r\noptimization viewpoint by establishing connections to the classic Frank-Wolfe\r\nalgorithm. Our analyses yields novel theoretical insights regarding the\r\nsufficient conditions for convergence, explicit rates, and algorithmic\r\nsimplifications. Since a lot of focus in previous works for variational\r\ninference has been on tractability, our work is especially important as a much\r\nneeded attempt to bridge the gap between probabilistic models and their\r\ncorresponding theoretical properties."}],"volume":84,"author":[{"id":"26cfd52f-2483-11ee-8040-88983bcc06d4","full_name":"Locatello, Francesco","first_name":"Francesco","orcid":"0000-0002-4850-0683","last_name":"Locatello"},{"full_name":"Khanna, Rajiv","first_name":"Rajiv","last_name":"Khanna"},{"last_name":"Ghosh","full_name":"Ghosh, Joydeep","first_name":"Joydeep"},{"last_name":"Rätsch","full_name":"Rätsch, Gunnar","first_name":"Gunnar"}],"date_published":"2018-04-15T00:00:00Z","status":"public","month":"04","year":"2018","_id":"14201","publisher":"ML Research Press","intvolume":" 84","date_created":"2023-08-22T14:15:20Z","language":[{"iso":"eng"}],"oa":1,"page":"464-472","conference":{"name":"AISTATS: Conference on Artificial Intelligence and Statistics","start_date":"2018-04-09","end_date":"2018-04-11","location":"Playa Blanca, Lanzarote"},"type":"conference","publication":"Proceedings of the 21st International Conference on Artificial Intelligence and Statistics"}