end-to-end learning

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

  • Yves Rychener
  • Daniel Kuhn
  • Tobias Sutter
    • Categories Stochastic Programming Tags , , ,

    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

  • Jincheng Yang
  • Luhao Zhang
  • Ningyuan Chen
  • Rui Gao
  • Ming Hu
    • Categories Optimization in Data Science, Robust Optimization, Stochastic Programming Tags , , ,

    We consider stochastic optimization with side information where, prior to decision-making, covariate data are available to inform better decisions. To hedge against data uncertainty while capturing the information structure revealed from the conditional distribution of random problem parameters given the covariate values, we propose a distributionally robust formulation based on causal transport distance. We derive … Read more