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A General Theoretical Framework for Learning Smallest Interpretable Models
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OBDDs, SDDs, and Circuits of Bounded Width: Completeness Matters
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Backdoor DNFs
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Compilation and Fast Model Counting beyond CNF
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Hardness of Random Reordered Encodings of Parity for Resolution and CDCL
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Learning Small Decision Trees for Data of Low Rank-Width
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SAT backdoors: Depth beats size
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SAT-based Decision Tree Learning for Large Data Sets
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SAT-Based Tree Decomposition with Iterative Cascading Policy Selection
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A Dynamic MaxSAT-based Approach to Directed Feedback Vertex Sets
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