200 citations · 450 across the 51 of their papers we have counts for
6 papers · 1 filter
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize
Ali Ugur Guler, Emir Demirovic, Jeffrey Chan +3
The predict+optimize problem combines machine learning ofproblem coefficients with a combinatorial optimization prob-lem that uses the predicted coefficients. While this problemcan…
Optimal Decision Lists using SAT
Jinqiang Yu, Alexey Ignatiev, Pierre Le Bodic +1
Decision lists are one of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, this machine learning model is…
Optimal Decision Trees for Nonlinear Metrics
Emir Demirović, Peter J. Stuckey
Nonlinear metrics, such as the F1-score, Matthews correlation coefficient, and Fowlkes-Mallows index, are often used to evaluate the performance of machine learning models, in part…
Computing Optimal Decision Sets with SAT
Jinqiang Yu, Alexey Ignatiev, Peter J. Stuckey +1
As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use…
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Emir Demirović, Anna Lukina, Emmanuel Hebrard +5
Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally us…
Encoding Linear Constraints into SAT
Ignasi Abío, Valentin Mayer-Eichberger, Peter Stuckey
Linear integer constraints are one of the most important constraints in combinatorial problems since they are commonly found in many practical applications. Typically, encodings to…