Data-driven robust solution schemes for sequential decision making

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Industrial Engineering

Grantor

University of Illinois Urbana-Champaign

Year dc:date

2025

Author dc:creator

• Park, Hyuk

Contributors dc:contributor

• 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

Statement dc:rights

• Copyright 2025 Hyuk Park

Language dc:language

en

Handle dc:identifier

https://hdl.handle.net/2142/132544

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/132544