4 accepted papers
Local search is a popular technique to solve combinatorial optimization problems efficiently. To escape local minima one generally uses metaheuristics or try to design large neighborhoods around the current best solution. A somewhat more black box approach consists in using an optimization solve…
This paper presents ddo, a generic and efficient library to solve constraint optimization problems with decision diagrams. To that end, our framework implements the branch-and-bound approach which has recently been introduced by Bergman et al., (2016) to solve dynamic programs to optimality. Our lib…
Decision trees are among the most popular classification models in machine learning. Traditionally, they are learned using greedy algorithms. However, such algorithms have their disadvantages: it is difficult to limit the size of the decision trees while maintaining a good classification accuracy, a…
Decision Trees (DTs) are widely used Machine Learning (ML) models with a broad range of applications. The interest in these models has increased even further in the context of Explainable AI (XAI), as decision trees of limited depth are very interpretable models. However, traditional algorithms for…