3 papers
math.OC2026
Uncertainty Quantification in Data-Driven Inverse Optimization via Bayesian Inference
Timothy C. Y. Chan, Nathan Sandholtz, Nasrin Yousefi
Inverse optimization (IO) is used to estimate unknown parameters of an optimization model from observed decisions. In the data-driven context, the estimated parameters are inherent…
cs.LG2025
Formal Verification of Markov Processes with Learned Parameters
Muhammad Maaz, Timothy C. Y. Chan
We introduce the problem of formally verifying properties of Markov processes where the parameters are given by the output of machine learning models. For a broad class of machine…
math.OC2024
Exact sensitivity analysis of Markov reward processes via algebraic geometry
Timothy C. Y. Chan, Muhammad Maaz
We introduce a new approach for deterministic sensitivity analysis of Markov reward processes, commonly used in cost-effectiveness analyses, via reformulation into a polynomial sys…