Optimal counterfactual explanations for k-Nearest Neighbors using Mathematical Optimization and Constraint Programming

\(\) Within the topic of explainable AI, counterfactual explanations to classifiers have received significant recent attention. We study counterfactual explanations that try to explain why a data point received an undesirable classification by providing the closest data point that would have received a desirable one. Within the context of one the simplest and most popular … Read more

Adaptive Partitioning for Chance-Constrained Problems with Finite Support

This paper studies chance-constrained stochastic optimization problems with finite support. It presents an iterative method that solves reduced-size chance-constrained models obtained by partitioning the scenario set. Each reduced problem is constructed to yield a bound on the optimal value of the original problem. We show how to adapt the partitioning of the scenario set so … Read more

New Benchmark Instances for the Capacitated Vehicle Routing Problem

The recent research on the CVRP is being slowed down by the lack of a good set of benchmark instances. The existing sets suff er from at least one of the following drawbacks: (i) became too easy for current algorithms; (ii) are too arti cial; (iii) are too homogeneous, not covering the wide range of characteristics found … Read more