--- _id: '68' 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. alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Alexander full_name: Zimin, Alexander id: 37099E9C-F248-11E8-B48F-1D18A9856A87 last_name: Zimin citation: ama: Zimin A. Learning from dependent data. 2018. doi:10.15479/AT:ISTA:TH1048 apa: Zimin, A. (2018). Learning from dependent data. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH1048 chicago: Zimin, Alexander. “Learning from Dependent Data.” Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:TH1048. ieee: A. Zimin, “Learning from dependent data,” Institute of Science and Technology Austria, 2018. ista: Zimin A. 2018. Learning from dependent data. Institute of Science and Technology Austria. mla: Zimin, Alexander. Learning from Dependent Data. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:TH1048. short: A. Zimin, Learning from Dependent Data, Institute of Science and Technology Austria, 2018. date_created: 2018-12-11T11:44:27Z date_published: 2018-09-01T00:00:00Z date_updated: 2023-09-07T12:29:07Z day: '01' ddc: - '004' - '519' degree_awarded: PhD department: - _id: ChLa doi: 10.15479/AT:ISTA:TH1048 ec_funded: 1 file: - access_level: open_access checksum: e849dd40a915e4d6c5572b51b517f098 content_type: application/pdf creator: dernst date_created: 2019-04-09T07:32:47Z date_updated: 2020-07-14T12:47:40Z file_id: '6253' file_name: 2018_Thesis_Zimin.pdf file_size: 1036137 relation: main_file - access_level: closed checksum: da092153cec55c97461bd53c45c5d139 content_type: application/zip creator: dernst date_created: 2019-04-09T07:32:47Z date_updated: 2020-07-14T12:47:40Z file_id: '6254' file_name: 2018_Thesis_Zimin_Source.zip file_size: 637490 relation: source_file file_date_updated: 2020-07-14T12:47:40Z has_accepted_license: '1' language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: '92' project: - _id: 2532554C-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '308036' name: Lifelong Learning of Visual Scene Understanding publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria publist_id: '7986' pubrep_id: '1048' status: public supervisor: - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 title: Learning from dependent data type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2018' ...