On Inverse Problems, Parameter Estimation, and Domain Generalization

ArXi:2506.06024v2 Announce Type: replace-cross Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence of machine learning methods, inversion of measurements has become a popular step in almost all physical applications, normally executed prior to downstream tasks that often involve parameter estimation. In this work, we propose a general framework for theoretical analysis of parameter estimation in inverse problem settings.