End-to-end Conditional Robust Optimization

arXiv:2403.04670v1 Announce Type: new
Abstract: The field of Contextual Optimization (CO) integrates machine learning and optimization to solve decision making problems under uncertainty. Recently, a risk sensitive variant of CO, known as Conditional Robust Optimization (CRO), combines uncertainty quantification with robust optimization in order to promote safety and reliability in high stake applications. Exploiting modern differentiable optimization methods, we propose a novel end-to-end approach to train a CRO model in a way that accounts for both the empirical risk of …

abstract applications arxiv cs.lg decision decision making differentiable machine machine learning making modern optimization promote quantification reliability risk robust safety solve type uncertainty