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Found 5 papers, 2 papers with code
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
Gradient boosting of prediction rules is an efficient approach to learn potentially interpretable yet accurate probabilistic models.
Multi-Target Search in Euclidean Space with Ray Shooting (Full Version)
The Euclidean shortest path problem (ESPP) is a well studied problem with many practical applications.
Better Short than Greedy: Interpretable Models through Optimal Rule Boosting
Rule ensembles are designed to provide a useful trade-off between predictive accuracy and model interpretability.
Optimal Decision Lists using SAT
Decision lists are one of the most easily explainable machine learning models.
Computing Optimal Decision Sets with SAT
Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large.
