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Recent work has considered optimal binary decision diagrams (BDD) as compact and accurate classification models, but has only focused on binary datasets and has ...
Sep 22, 2023 · In this work, we present a SAT-based encoding for a multi-terminal variant of BDDs (MTBDDs) that incorporates a state-of-the-art direct encoding of numerical ...
Dec 21, 2023 · CP2023: paper "SAT-Based Learning of Compact Binary Decision Diagrams for Classification" by Pouya Shati, Eldan Cohen and Sheila McIlraith.
Recent work has considered optimal binary decision diagrams (BDD) as compact and accurate classification models, but has only focused on binary datasets and has ...
SAT-Based Learning of Compact Binary Decision Diagrams for Classification. Shati, P., Cohen, E., & McIlraith, S. A. In Proceedings of the 29th International ...
Mar 21, 2022 · In this paper, we first propose SAT-based models for learning optimal BDDs (in terms of the number of features) that classify all input examples.
In this paper, we first propose SAT-based models for learning optimal BDDs (in terms of the number of features) that classify all input exam- ples. Then, we ...
SAT-Based Learning of Compact Binary Decision Diagrams for Classification. P Shati, E Cohen, S McIlraith. 29th International Conference on Principles and ...
Apr 11, 2024 · In this article we propose the use of binary decision diagrams (BDDs) as an interpretable ML model. BDDs can be deemed as interpretable as decision trees (DTs).
Our work proposes preliminary inroads in two main directions: (a) proposing a SAT-based model for computing a decision tree as the smallest Reduced Ordered ...