用可微真值表学稀疏规则，让模型既准又易懂。
TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
- 用可微真值表做神经单元，自动学稀疏连接
- 在28个数据集上比现有方法更准且规则更简单
- 能直接转成人类看得懂的逻辑公式，适合高风险场景
可解释机器学习在需要问责、透明和信任的高风险领域至关重要。尽管基于规则的模型提供全局精确的可解释性，但同时实现高预测性能与低复杂度、人类可理解的规则仍具挑战。为此，我们提出TT-Sparse，一种灵活的神经构建块，利用可微真值表作为节点来学习稀疏有效的连接。其关键贡献是引入一种新的软TopK算子，结合直通估计，以端到端可微方式实现离散、基数受限的特征选择。重要的是，前向传播保持稀疏，支持高效计算和精确符号规则提取。结果，每个节点（及整个模型）可通过Quine-McCluskey最小化精确转换为紧凑的全局可解释DNF/CNF布尔公式。在涵盖二分类、多分类和回归任务的28个数据集上的广泛实验证明，所学稀疏规则在预测性能上优于现有最先进方法，且复杂度更低。
原文摘要 · Abstract (English)
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 differentiable manner. Crucially, the forward pass remains sparse, enabling efficient computation and exact symbolic rule extraction. As a result, each node (and the entire model) can be transformed exactly into compact, globally interpretable DNF/CNF Boolean formulas via Quine-McCluskey minimization. Extensive empirical results across 28 datasets spanning binary, multiclass, and regression tasks show that the learned sparse rules exhibit superior predictive performance with lower complexity compared to existing state-of-the-art methods.
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