{"isi":1,"project":[{"call_identifier":"FP7","grant_number":"308036","_id":"2532554C-B435-11E9-9278-68D0E5697425","name":"Lifelong Learning of Visual Scene Understanding"}],"intvolume":"        54","publist_id":"6261","volume":54,"year":"2017","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.","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.","short":"A. Zimin, C. Lampert, in:, ML Research Press, 2017, pp. 213–222.","mla":"Zimin, Alexander, and Christoph Lampert. <i>Learning Theory for Conditional Risk Minimization</i>. Vol. 54, ML Research Press, 2017, pp. 213–22.","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."},"conference":{"name":"AISTATS: Artificial Intelligence and Statistics","start_date":"2017-04-20","location":"Fort Lauderdale, FL, United States","end_date":"2017-04-22"},"ec_funded":1,"_id":"1108","language":[{"iso":"eng"}],"author":[{"id":"37099E9C-F248-11E8-B48F-1D18A9856A87","full_name":"Zimin, Alexander","last_name":"Zimin","first_name":"Alexander"},{"orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","last_name":"Lampert","first_name":"Christoph"}],"page":"213 - 222","external_id":{"isi":["000509368500024"]},"quality_controlled":"1","title":"Learning theory for conditional risk minimization","status":"public","alternative_title":["PMLR"],"oa_version":"Submitted Version","oa":1,"date_created":"2018-12-11T11:50:11Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"ML Research Press","publication_status":"published","date_published":"2017-04-01T00:00:00Z","department":[{"_id":"ChLa"}],"main_file_link":[{"url":"http://proceedings.mlr.press/v54/zimin17a/zimin17a.pdf","open_access":"1"}],"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."}],"type":"conference","day":"01","article_processing_charge":"No","month":"04","date_updated":"2023-10-17T10:01:12Z"}