# Towards Robust Interpretable Surrogates for Optimization

arXiv: https://arxiv.org/abs/2412.01264 · HTML: https://mlsanity.com/paper/2412.01264/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Counterfactual Explanations for Linear Optimization](https://mlsanity.com/paper/2405.15431/) (2024)
- [Improving Stability in Decision Tree Models](https://mlsanity.com/paper/2305.17299/) (2023)
- [Feature-Based Interpretable Surrogates for Optimization](https://mlsanity.com/paper/2409.01869/) (2024)

## Built on by (most cited first, 1 of 1)

- [Decision-Value Attribution in Predict-then-Optimize Systems](https://mlsanity.com/paper/2606.29878/) (2026)

## Abstract (arXiv)

An important factor in the practical implementation of optimization models is the acceptance by the intended users. This is influenced among other factors by the interpretability of the solution process. Decision rules that meet this requirement can be generated using the framework for inherently interpretable optimization models. In practice, there is often uncertainty about the parameters of an optimization problem. An established way to deal with this challenge is the concept of robust optimization. The goal of our work is to combine both concepts: to create decision trees as surrogates for the optimization process that are more robust to perturbations and still inherently interpretable. For this purpose we present suitable models based on different variants to model uncertainty, and solution methods. Furthermore, the applicability of heuristic methods to perform this task is evaluated. Both approaches are compared with the existing framework for inherently interpretable optimization models.