让机器学习规则自动学习并排序，提高医疗等高风险场景的可解释性。
Neuro-Symbolic Rule Lists
- 端到端可训练框架，统一特征离散化、规则生成和顺序学习
- 在多个数据集上优于传统方法，能学习简单与复杂规则组合
- 无需预处理或规则大小限制，适合医疗等需透明决策的领域
部署于医疗等敏感领域的机器学习模型必须具备可解释性以确保问责与公平。规则列表（如若年龄<35且既往史>0则再犯=真，否则若下个条件……）提供完全透明，适用于高风险决策。然而，学习此类规则列表面临重大挑战：现有基于组合优化的方法需特征预离散化，并对规则长度施加限制；神经符号方法虽采用更可扩展的连续优化，但仍需预离散化，且优化过程不稳定。为此，我们提出NeuRules，一种将离散化、规则学习与规则顺序统一于单一可微框架的端到端可训练模型。通过连续松弛规则列表学习问题，并借助温度退火实现向严格规则列表收敛。NeuRules同时学习各特征的离散化及其组合成合取规则，无需任何预处理或约束。大量实验表明，NeuRules在多种数据集上持续超越组合与神经符号方法，有效学习简单与复杂规则及其顺序。
原文摘要 · Abstract (English)
Machine learning models deployed in sensitive areas such as healthcare must be interpretable to ensure accountability and fairness. Rule lists (if Age < 35 $\wedge$ Priors > 0 then Recidivism = True, else if Next Condition . . . ) offer full transparency, making them well-suited for high-stakes decisions. However, learning such rule lists presents significant challenges. Existing methods based on combinatorial optimization require feature pre-discretization and impose restrictions on rule size. Neuro-symbolic methods use more scalable continuous optimization yet place similar pre-discretization constraints and suffer from unstable optimization. To address the existing limitations, we introduce NeuRules, an end-to-end trainable model that unifies discretization, rule learning, and rule order into a single differentiable framework. We formulate a continuous relaxation of the rule list learning problem that converges to a strict rule list through temperature annealing. NeuRules learns both the discretizations of individual features, as well as their combination into conjunctive rules without any pre-processing or restrictions. Extensive experiments demonstrate that NeuRules consistently outperforms both combinatorial and neuro-symbolic methods, effectively learning simple and complex rules, as well as their order, across a wide range of datasets.
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