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math.OC2024★ 1 cited
Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources
Yves Rychener, Adrian Esteban-Perez, Juan M. Morales +1
We study decision problems under uncertainty, where the decision-maker has access to data sources that carry {\em biased} information about the underlying risk factors. The bia…
math.OC2023★ 4 cited
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
Yves Rychener, Daniel Kuhn, Tobias Sutter
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…