[{"title":"Learning from dependent data","publist_id":"7986","degree_awarded":"PhD","author":[{"first_name":"Alexander","last_name":"Zimin","full_name":"Zimin, Alexander","id":"37099E9C-F248-11E8-B48F-1D18A9856A87"}],"day":"01","file":[{"file_id":"6253","date_updated":"2020-07-14T12:47:40Z","checksum":"e849dd40a915e4d6c5572b51b517f098","date_created":"2019-04-09T07:32:47Z","access_level":"open_access","file_name":"2018_Thesis_Zimin.pdf","file_size":1036137,"content_type":"application/pdf","relation":"main_file","creator":"dernst"},{"date_created":"2019-04-09T07:32:47Z","access_level":"closed","date_updated":"2020-07-14T12:47:40Z","file_id":"6254","checksum":"da092153cec55c97461bd53c45c5d139","content_type":"application/zip","relation":"source_file","file_size":637490,"creator":"dernst","file_name":"2018_Thesis_Zimin_Source.zip"}],"ec_funded":1,"article_processing_charge":"No","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","publisher":"Institute of Science and Technology Austria","department":[{"_id":"ChLa"}],"doi":"10.15479/AT:ISTA:TH1048","publication_identifier":{"issn":["2663-337X"]},"pubrep_id":"1048","language":[{"iso":"eng"}],"project":[{"_id":"2532554C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Lifelong Learning of Visual Scene Understanding","grant_number":"308036"}],"file_date_updated":"2020-07-14T12:47:40Z","date_created":"2018-12-11T11:44:27Z","page":"92","abstract":[{"lang":"eng","text":"The most common assumption made in statistical learning theory is the assumption of the independent and identically distributed (i.i.d.) data. While being very convenient mathematically, it is often very clearly violated in practice. This disparity between the machine learning theory and applications underlies a growing demand in the development of algorithms that learn from dependent data and theory that can provide generalization guarantees similar to the independent situations. This thesis is dedicated to two variants of dependencies that can arise in practice. One is a dependence on the level of samples in a single learning task. Another dependency type arises in the multi-task setting when the tasks are dependent on each other even though the data for them can be i.i.d. In both cases we model the data (samples or tasks) as stochastic processes and introduce new algorithms for both settings that take into account and exploit the resulting dependencies. We prove the theoretical guarantees on the performance of the introduced algorithms under different evaluation criteria and, in addition, we compliment the theoretical study by the empirical one, where we evaluate some of the algorithms on two real world datasets to highlight their practical applicability."}],"date_updated":"2023-09-07T12:29:07Z","type":"dissertation","month":"09","oa_version":"Published Version","_id":"68","year":"2018","supervisor":[{"last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"ddc":["004","519"],"date_published":"2018-09-01T00:00:00Z","oa":1,"publication_status":"published","has_accepted_license":"1","citation":{"ieee":"A. Zimin, “Learning from dependent data,” Institute of Science and Technology Austria, 2018.","chicago":"Zimin, Alexander. “Learning from Dependent Data.” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:TH1048\">https://doi.org/10.15479/AT:ISTA:TH1048</a>.","short":"A. Zimin, Learning from Dependent Data, Institute of Science and Technology Austria, 2018.","ama":"Zimin A. Learning from dependent data. 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:TH1048\">10.15479/AT:ISTA:TH1048</a>","ista":"Zimin A. 2018. Learning from dependent data. Institute of Science and Technology Austria.","mla":"Zimin, Alexander. <i>Learning from Dependent Data</i>. Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:TH1048\">10.15479/AT:ISTA:TH1048</a>.","apa":"Zimin, A. (2018). <i>Learning from dependent data</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:TH1048\">https://doi.org/10.15479/AT:ISTA:TH1048</a>"},"alternative_title":["ISTA Thesis"],"status":"public"},{"title":"Learning theory for conditional risk minimization","publist_id":"6261","author":[{"full_name":"Zimin, Alexander","id":"37099E9C-F248-11E8-B48F-1D18A9856A87","first_name":"Alexander","last_name":"Zimin"},{"first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph"}],"day":"01","article_processing_charge":"No","ec_funded":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"ML Research Press","department":[{"_id":"ChLa"}],"quality_controlled":"1","isi":1,"conference":{"end_date":"2017-04-22","start_date":"2017-04-20","location":"Fort Lauderdale, FL, United States","name":"AISTATS: Artificial Intelligence and Statistics"},"language":[{"iso":"eng"}],"project":[{"call_identifier":"FP7","_id":"2532554C-B435-11E9-9278-68D0E5697425","name":"Lifelong Learning of Visual Scene Understanding","grant_number":"308036"}],"volume":54,"date_created":"2018-12-11T11:50:11Z","page":"213 - 222","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."}],"date_updated":"2023-10-17T10:01:12Z","oa_version":"Submitted Version","month":"04","type":"conference","_id":"1108","year":"2017","date_published":"2017-04-01T00:00:00Z","main_file_link":[{"url":"http://proceedings.mlr.press/v54/zimin17a/zimin17a.pdf","open_access":"1"}],"publication_status":"published","oa":1,"intvolume":"        54","citation":{"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.","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.","ama":"Zimin A, Lampert C. Learning theory for conditional risk minimization. In: Vol 54. ML Research Press; 2017:213-222.","mla":"Zimin, Alexander, and Christoph Lampert. <i>Learning Theory for Conditional Risk Minimization</i>. Vol. 54, ML Research Press, 2017, pp. 213–22.","ista":"Zimin A, Lampert C. 2017. Learning theory for conditional risk minimization. AISTATS: Artificial Intelligence and Statistics, PMLR, vol. 54, 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."},"alternative_title":["PMLR"],"status":"public","external_id":{"isi":["000509368500024"]}}]