Many systems in science, engineering, and machine learning evolve according to underlying laws that are only partially known and observed. Understanding their behavior therefore requires inferring these laws from incomplete data. This thesis investigates this inverse problem across three settings, including networked dynamical systems, neural network training, and in-context learning with Markov chains.
Date
