2026-09-19T09:06:12Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/132544 2026-02-20 col_2142_5131 col_2142_16359 com_2142_5130 com_2142_16358 com_2142_234

application/pdf en text 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 Data-driven robust solution schemes for sequential decision making Park, Hyuk 2025-12-02 Hanasusanto, Grani Adiwena Dayanıklı, Gökçe Etesami, Rasoul Zhang, Shixuan Stochastic Optimization Distributionally Robust Optimization Sequential Decision Making Multistage Stochastic Programming Path Integral Control System Identification eng 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. 2025-12 Thesis https://hdl.handle.net/2142/132544 Copyright 2025 Hyuk Park Industrial&Enterprise Sys Eng Industrial Engineering University of Illinois Urbana-Champaign Ph.D. Dissertation