CPAIOR 2022 master class by Brandon Amos.
Abstract: This talk tours the foundations and applications of optimization-based models for machine learning. Optimization is a widely-used modeling paradigm for solving non-trivial reasoning operations and brings precise domain-specific modeling priors into end-to-end machine learning pipelines that are otherwise typically large parameterized black-box functions. We will discuss how to integrate optimization as a differentiable layer and start simple with constrained, continuous, convex problems in Euclidean spaces. We will then move onto active research topics that expand beyond these core components into non-convex, non-Euclidean, discrete, and combinatorial spaces. Throughout all of these, we will consider applications in control, reinforcement learning, and vision.
