{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125840"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125840","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bandits in autoregressive Markov models","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","abstract_has_math":false,"creators":["Sun, Yicheng"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Katselis, Dimitrios"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-19","date_published":"2024-07-19","updated_at":"2026-07-22T22:25:02Z","subjects":["Bandit Algorithms"],"languages":["en","eng"],"rights":["Copyright 2024 Yicheng Sun"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125840","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Katselis, Dimitrios"]},{"key":"dc:creator","label":"Author","values":["Sun, Yicheng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-19","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bandit Algorithms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Yicheng Sun"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125840"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","The student, Yicheng Sun, accepted the attached license on 2024-07-19 at 16:45.","The student, Yicheng Sun, submitted this Thesis for approval on 2024-07-19 at 16:54.","This Thesis was approved for publication on 2024-07-19 at 16:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21159 on 2025-02-04 at 21:26:02","This thesis explores algorithms used in stochastic and Markovian multi-armed bandits, along with their applications in autoregressive models with a graphical structure. It begins by introducing some background of Markov chains, including concentration properties and variations of the Chernoff bounds. The work further elucidates the setup of multi-armed bandits, emphasizing fundamental concepts such as the exploration-exploitation tradeoff and regret minimization. Established algorithms in stochastic bandits, like the Upper Confidence Bound and epsilon-Greedy are analysed as well as their adaptations for Markovian environments. The thesis then introduces binary valued proccesses with a graphical structure, such as the ALARM and the BAR models, and assesses their structural implications for bandit problems. Combining the two topics, it formulates a bandit problem based on the BAR model and applies these algorithms to minimize the regret. A comprehensive analysis of various algorithms is conducted along with experimental validations. These experiments support the theoretical assertions, showing the practical robustness and effectiveness of Markovian bandit algorithms."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Bandits in autoregressive Markov models"]}]}],"canonical_facts":{"dc:contributor":["Katselis, Dimitrios"],"dc:creator":["Sun, Yicheng"],"dc:date":["2024-07-19","2024-08"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","The student, Yicheng Sun, accepted the attached license on 2024-07-19 at 16:45.","The student, Yicheng Sun, submitted this Thesis for approval on 2024-07-19 at 16:54.","This Thesis was approved for publication on 2024-07-19 at 16:59.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21159 on 2025-02-04 at 21:26:02","This thesis explores algorithms used in stochastic and Markovian multi-armed bandits, along with their applications in autoregressive models with a graphical structure. It begins by introducing some background of Markov chains, including concentration properties and variations of the Chernoff bounds. The work further elucidates the setup of multi-armed bandits, emphasizing fundamental concepts such as the exploration-exploitation tradeoff and regret minimization. Established algorithms in stochastic bandits, like the Upper Confidence Bound and epsilon-Greedy are analysed as well as their adaptations for Markovian environments. The thesis then introduces binary valued proccesses with a graphical structure, such as the ALARM and the BAR models, and assesses their structural implications for bandit problems. Combining the two topics, it formulates a bandit problem based on the BAR model and applies these algorithms to minimize the regret. A comprehensive analysis of various algorithms is conducted along with experimental validations. These experiments support the theoretical assertions, showing the practical robustness and effectiveness of Markovian bandit algorithms."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125840"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Yicheng Sun"],"dc:subject":["Bandit Algorithms"],"dc:title":["Bandits in autoregressive Markov models"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}