TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
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 introduce TT-Sparse, a flexible neural building block that leverages differentiable truth tables as nodes to learn sparse, effective connections. A key contribution of our approach is a new soft TopK operator with straight-through estimation for learning discrete, cardinality-constrained feature selection in an end-to-end differentiab...
| Authors | Hans Farrell Soegeng, Sarthak Ketanbhai Modi, Thomas Peyrin |
|---|---|
| Primary category | cs.LG |
| Published | 8 March 2026 |
| Last updated at source | 6 July 2026 |
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