In this section
@article{Belbasi:2026:10.1287/msom.2023.0738,
author = {Belbasi, R and Selvi, A and Wiesemann, W},
doi = {10.1287/msom.2023.0738},
journal = {Manufacturing & Service Operations Management},
title = {It’s all in the mix: Wasserstein classification and regression with mixed features},
url = {http://dx.doi.org/10.1287/msom.2023.0738},
year = {2026}
}
TY - JOUR
AB - Problem definition: A key challenge in supervised learning is data scarcity, which can cause prediction models to overfit to the training data and perform poorly out of sample. A contemporary approach to combat overfitting is offered by distributionally robust problem formulations that consider all data-generating distributions close to the empirical distribution derived from historical samples, where “closeness” is determined by the Wasserstein distance. Although such formulations show significant promise in prediction tasks where all input features are continuous, they scale exponentially when discrete features are present. Methodology/results: We demonstrate that distributionally robust mixed-feature classification and regression problems can indeed be solved in polynomial time. Our proof relies on classical ellipsoid method-based solution schemes that do not scale well in practice. To overcome this limitation, we develop a practically efficient (yet, in the worst case, exponential-time) cutting-plane-based algorithm that admits a polynomial-time separation oracle, despite the presence of exponentially many constraints. We compare our method against alternative techniques both theoretically and empirically on standard benchmark instances. Managerial implications: Data-driven operations management problems often involve prediction models with discrete features. We develop and analyze distributionally robust prediction models that faithfully account for the presence of discrete features, and we demonstrate that our models can significantly outperform existing methods that are agnostic to the presence of discrete features both theoretically and on standard benchmark instances.
AU - Belbasi,R
AU - Selvi,A
AU - Wiesemann,W
DO - 10.1287/msom.2023.0738
PY - 2026///
SN - 1523-4614
TI - It’s all in the mix: Wasserstein classification and regression with mixed features
T2 - Manufacturing & Service Operations Management
UR - http://dx.doi.org/10.1287/msom.2023.0738
ER -