AI RESEARCH
TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
arXiv CS.LG
•
ArXi:2603.07606v1 Announce Type: new Interpretable machine learning is essential in high-stakes domains where decision-making requires accountability, transparency, and trust. While rule-based models offer global and exact interpretability, learning rule sets that simultaneously achieve high predictive performance and low, human-understandable complexity remains challenging. To address this, we