{"main_file_link":[{"open_access":"1","url":"http://proceedings.mlr.press/v54/zimin17a/zimin17a.pdf"}],"publist_id":"6261","date_updated":"2023-10-17T10:01:12Z","ec_funded":1,"citation":{"ama":"Zimin A, Lampert C. Learning theory for conditional risk minimization. In: Vol 54. ML Research Press; 2017:213-222.","apa":"Zimin, A., &#38; Lampert, C. (2017). Learning theory for conditional risk minimization (Vol. 54, pp. 213–222). Presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States: ML Research Press.","ieee":"A. Zimin and C. Lampert, “Learning theory for conditional risk minimization,” presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States, 2017, vol. 54, pp. 213–222.","short":"A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222.","ista":"Zimin A, Lampert C. 2017. Learning theory for conditional risk minimization. AISTATS: Artificial Intelligence and Statistics, PMLR, vol. 54, 213–222.","chicago":"Zimin, Alexander, and Christoph Lampert. “Learning Theory for Conditional Risk Minimization,” 54:213–22. ML Research Press, 2017.","mla":"Zimin, Alexander, and Christoph Lampert. <i>Learning Theory for Conditional Risk Minimization</i>. Vol. 54, ML Research Press, 2017, pp. 213–22."},"type":"conference","title":"Learning theory for conditional risk minimization","intvolume":"        54","project":[{"grant_number":"308036","_id":"2532554C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Lifelong Learning of Visual Scene Understanding"}],"alternative_title":["PMLR"],"conference":{"end_date":"2017-04-22","start_date":"2017-04-20","location":"Fort Lauderdale, FL, United States","name":"AISTATS: Artificial Intelligence and Statistics"},"isi":1,"department":[{"_id":"ChLa"}],"year":"2017","abstract":[{"lang":"eng","text":"In this work we study the learnability of stochastic processes with respect to the conditional risk, i.e. the existence of a learning algorithm that improves its next-step performance with the amount of observed data. We introduce a notion of pairwise discrepancy between conditional distributions at different times steps and show how certain properties of these discrepancies can be used to construct a successful learning algorithm. Our main results are two theorems that establish criteria for learnability for many classes of stochastic processes, including all special cases studied previously in the literature."}],"quality_controlled":"1","oa":1,"volume":54,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2018-12-11T11:50:11Z","status":"public","day":"01","_id":"1108","page":"213 - 222","language":[{"iso":"eng"}],"external_id":{"isi":["000509368500024"]},"oa_version":"Submitted Version","publisher":"ML Research Press","author":[{"last_name":"Zimin","first_name":"Alexander","full_name":"Zimin, Alexander","id":"37099E9C-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","first_name":"Christoph","last_name":"Lampert"}],"publication_status":"published","article_processing_charge":"No","date_published":"2017-04-01T00:00:00Z","month":"04"}