{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132544"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132544","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Data-driven robust solution schemes for sequential decision making","abstract":"This dissertation develops robust and data-efficient methodologies for sequential decision making under uncertainty, motivated by challenges arising in operations research, control, and machine learning. Classical approaches such as sample average approximation—also referred to as empirical risk minimization in the machine learning literature—often suffer from poor out-of-sample performance when data is limited. To address this issue, the dissertation proposes a data-efficient alternative to sample average approximation for multistage stochastic programming with Markovian uncertainty and introduces robust and distributionally robust optimization frameworks for two additional problem domains: fairness-aware stochastic optimal control and system identification from a single trajectory. The proposed robust formulations yield tractable optimization problems that can be efficiently solved using off-the-shelf commercial solvers while providing rigorous non-asymptotic performance guarantees. In several chapters, our analysis further uncovers interesting connections between robustification and regularization, the latter being a widely used heuristic in machine learning and control. These contributions advance both the theory and practice of robust learning and control, offering reliable and scalable solutions for data-driven sequential decision making under uncertainty.","abstract_html":"This dissertation develops robust and data-efficient methodologies for sequential decision making under uncertainty, motivated by challenges arising in operations research, control, and machine learning. Classical approaches such as sample average approximation—also referred to as empirical risk minimization in the machine learning literature—often suffer from poor out-of-sample performance when data is limited. To address this issue, the dissertation proposes a data-efficient alternative to sample average approximation for multistage stochastic programming with Markovian uncertainty and introduces robust and distributionally robust optimization frameworks for two additional problem domains: fairness-aware stochastic optimal control and system identification from a single trajectory. The proposed robust formulations yield tractable optimization problems that can be efficiently solved using off-the-shelf commercial solvers while providing rigorous non-asymptotic performance guarantees. In several chapters, our analysis further uncovers interesting connections between robustification and regularization, the latter being a widely used heuristic in machine learning and control. 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Classical approaches such as sample average approximation—also referred to as empirical risk minimization in the machine learning literature—often suffer from poor out-of-sample performance when data is limited. To address this issue, the dissertation proposes a data-efficient alternative to sample average approximation for multistage stochastic programming with Markovian uncertainty and introduces robust and distributionally robust optimization frameworks for two additional problem domains: fairness-aware stochastic optimal control and system identification from a single trajectory. The proposed robust formulations yield tractable optimization problems that can be efficiently solved using off-the-shelf commercial solvers while providing rigorous non-asymptotic performance guarantees. In several chapters, our analysis further uncovers interesting connections between robustification and regularization, the latter being a widely used heuristic in machine learning and control. These contributions advance both the theory and practice of robust learning and control, offering reliable and scalable solutions for data-driven sequential decision making under uncertainty.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Hyuk Park, accepted the attached license on 2025-11-29 at 23:50.","The student, Hyuk Park, submitted this Dissertation for approval on 2025-11-30 at 00:17.","This Dissertation was approved for publication on 2025-12-02 at 16:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22982 on 2026-02-19 at 18:25:42"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-driven robust solution schemes for sequential decision making"]}]}],"canonical_facts":{"dc:contributor":["Hanasusanto, Grani Adiwena","Dayanıklı, Gökçe","Etesami, Rasoul","Zhang, Shixuan"],"dc:creator":["Park, Hyuk"],"dc:date":["2025-12","2025-12-02"],"dc:description":["This dissertation develops robust and data-efficient methodologies for sequential decision making under uncertainty, motivated by challenges arising in operations research, control, and machine learning. Classical approaches such as sample average approximation—also referred to as empirical risk minimization in the machine learning literature—often suffer from poor out-of-sample performance when data is limited. To address this issue, the dissertation proposes a data-efficient alternative to sample average approximation for multistage stochastic programming with Markovian uncertainty and introduces robust and distributionally robust optimization frameworks for two additional problem domains: fairness-aware stochastic optimal control and system identification from a single trajectory. The proposed robust formulations yield tractable optimization problems that can be efficiently solved using off-the-shelf commercial solvers while providing rigorous non-asymptotic performance guarantees. In several chapters, our analysis further uncovers interesting connections between robustification and regularization, the latter being a widely used heuristic in machine learning and control. These contributions advance both the theory and practice of robust learning and control, offering reliable and scalable solutions for data-driven sequential decision making under uncertainty.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Hyuk Park, accepted the attached license on 2025-11-29 at 23:50.","The student, Hyuk Park, submitted this Dissertation for approval on 2025-11-30 at 00:17.","This Dissertation was approved for publication on 2025-12-02 at 16:27.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22982 on 2026-02-19 at 18:25:42"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132544"],"dc:language":["en"],"dc:rights":["Copyright 2025 Hyuk Park"],"dc:subject":["Stochastic Optimization","Distributionally Robust Optimization","Sequential Decision Making","Multistage Stochastic Programming","Path Integral Control","System Identification"],"dc:title":["Data-driven robust solution schemes for sequential decision making"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}