Strides

A General Theoretical Framework for Learning Smallest Interpretable Models
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Computing Probabilistic Explanations for ML Models: Fixed-Parameter Algorithms
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From Data Completion to Problems on Hypercubes: A Parameterized Analysis of the Independent Set Problem
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OBDDs, SDDs, and Circuits of Bounded Width: Completeness Matters
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Analyzing Reformulation Performance in Core-Guided MaxSAT Solving
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Balancing Latin Rectangles with LLM-Generated Streamliners
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Bridging Language Models and Symbolic Solvers via the Model Context Protocol
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Extracting Problem Structure with LLMs for Optimized SAT Local Search
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Generating Streamlining Constraints with Large Language Models
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Optimal Decision Tree Pruning Revisited: Algorithms and Complexity
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