# Feature-Based Interpretable Surrogates for Optimization

arXiv: https://arxiv.org/abs/2409.01869 · HTML: https://mlsanity.com/paper/2409.01869/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 3 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [A Survey of Contextual Optimization Methods for Decision Making under Uncertainty](https://mlsanity.com/paper/2306.10374/) (2023)
- [Interpretable Optimal Stopping](https://mlsanity.com/paper/1812.07211/) (2018)
- [Learning Optimal Prescriptive Trees from Observational Data](https://mlsanity.com/paper/2108.13628/) (2021)
- [Algorithms and Uncertainty Sets for Data-Driven Robust Shortest Path Problems](https://mlsanity.com/paper/1802.04149/) (2018)
- [Robust Optimization with Decision-Dependent Information Discovery](https://mlsanity.com/paper/2004.08490/) (2020)
- [Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning](https://mlsanity.com/paper/2109.03323/) (2021)

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

- [Towards Robust Interpretable Surrogates for Optimization](https://mlsanity.com/paper/2412.01264/) (2024)

## Abstract (arXiv)

For optimization models to be used in practice, it is crucial that users trust the results. A key factor in this aspect is the interpretability of the solution process. A previous framework for inherently interpretable optimization models used decision trees to map instances to solutions of the underlying optimization model. Based on this work, we investigate how we can use more general optimization rules to further increase interpretability and, at the same time, give more freedom to the decision-maker. The proposed rules do not map to a concrete solution but to a set of solutions characterized by common features. To find such optimization rules, we present an exact methodology using mixed-integer programming formulations as well as heuristics. We also outline the challenges and opportunities that these methods present. In particular, we demonstrate the improvement in solution quality that our approach offers compared to existing interpretable surrogates for optimization, and we discuss the relationship between interpretability and performance. These findings are supported by experiments using both synthetic and real-world data.