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

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 posterior Bayes action map. Building on the insights of this analysis, we then propose new end-to-end learning algorithms for training decision maps that output solutions of empirical risk … Read more

Decision-making with Side Information: A Causal Transport Robust Approach

We consider stochastic optimization with side information where, prior to decision making, covariate data are available to inform better decisions.¬†In particular, we propose to consider a distributionally robust formulation based on causal transport distance.¬†Compared with divergence and Wasserstein metric, the causal transport distance is better at capturing the information structure revealed from the conditional distribution … Read more