Tabea E. Röber

Rule Generation for Classification: Scalability, Interpretability, and Fairness

2021 · arXiv (Cornell University)

We introduce a new rule-based optimization method for classification with constraints. The proposed method leverages column generation for linear programming, and hence, is scalable to large datasets. The resulting pricing subproblem is shown to be …

Finding Regions of Counterfactual Explanations via Robust Optimization

2023 · arXiv (Cornell University)

Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a minimal perturbed data point for which the decision of the model changes. …