644 citations · 812 across the 12 of their papers we have counts for
12 papers
Counterfactual Explanations for Linear Optimization
Jannis Kurtz, Ş. İlker Birbil, Dick den Hertog
The concept of counterfactual explanations (CE) has emerged as one of the important concepts to understand the inner workings of complex AI systems. In this paper, we translate the…
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
Segev Wasserkrug, Leonard Boussioux, Dick den Hertog +4
Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to impor…
Finding Regions of Counterfactual Explanations via Robust Optimization
Donato Maragno, Jannis Kurtz, Tabea E. Röber +3
Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a m…
Robust Upper Bounds for Adversarial Training
Dimitris Bertsimas, Xavier Boix, Kimberly Villalobos Carballo +1
Many state-of-the-art adversarial training methods for deep learning leverage upper bounds of the adversarial loss to provide security guarantees against adversarial attacks. Yet,…
Mixed-Integer Optimization with Constraint Learning
Donato Maragno, Holly Wiberg, Dimitris Bertsimas +3
We establish a broad methodological foundation for mixed-integer optimization with learned constraints. We propose an end-to-end pipeline for data-driven decision making in which c…
Optimization with Constraint Learning: A Framework and Survey
Adejuyigbe Fajemisin, Donato Maragno, Dick den Hertog
Many real-life optimization problems frequently contain one or more constraints or objectives for which there are no explicit formulas. If data is however available, these data can…
Pareto Adaptive Robust Optimality via a Fourier-Motzkin Elimination Lens
Dimitris Bertsimas, Stefan ten Eikelder, Dick den Hertog +1
We formalize the concept of Pareto Adaptive Robust Optimality (PARO) for linear Adaptive Robust Optimization (ARO) problems. A worst-case optimal solution pair of here-and-now deci…
Extending the Scope of Robust Quadratic Optimization
Ahmadreza Marandi, Aharon Ben-Tal, Dick den Hertog +1
We derive computationally tractable formulations of the robust counterparts of convex quadratic and conic quadratic constraints that are concave in matrix-valued uncertain paramete…
Approximating the Pareto set of multiobjective linear programs via Robust Optimization
Bram L. Gorissen, Dick den Hertog
We consider problems with multiple linear objectives and linear constraints and use Adjustable Robust Optimization and Polynomial Optimization as tools to approximate the Pareto se…
Robust counterparts of inequalities containing sums of maxima of linear functions
Bram L. Gorissen, Dick den Hertog
This paper addresses the robust counterparts of optimization problems containing sums of maxima of linear functions. These problems include many practical problems, e.g.~problems w…
A Practical Guide to Robust Optimization
Bram L. Gorissen, Ihsan Yanıkoğlu, Dick den Hertog
Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored…
Mixed integer programming improves comprehensibility and plan quality in inverse optimization of prostate HDR-brachytherapy
Bram L. Gorissen, Dick den Hertog, Aswin L. Hoffmann
Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions…