End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Yves Rychener, Daniel Kuhn, Tobias Sutter

Abstract: We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a post...