用记忆复用和规则转换加速模型解释，提升效率且保持准确。
MAnchors: Memorization-Based Acceleration of Anchors via Rule Reuse and Transformation
- 通过存储并复用先前解释的中间结果，减少重复计算。
- 在多个数据集上将解释生成时间显著缩短，同时保持解释精度。
- 适合需要快速生成解释的实时应用场景，如医疗或金融风控。
Anchors 是一种流行的局部、模型无关的解释方法，但其应用受限于计算效率低下。为解决此问题，我们提出一种基于记忆的加速框架，在保持解释保真度与可解释性的前提下提升 Anchors 的速度。该方法利用 Anchors 算法的迭代特性，通过存储低精度但高覆盖范围的规则，并引入规则转换机制以适应新输入：水平变换通过替换特征将预训练解释适配当前输入；垂直变换则逐步精细化通用解释，直至满足当前输入的精确性要求。我们在表格、文本和图像数据集上进行了评估，结果表明该方法显著降低了解释生成时间，同时维持了高质量的解释性能，从而推动 Anchors 在时间敏感场景中的实际应用。
原文摘要 · Abstract (English)
Anchors is a popular local model-agnostic explanation technique whose applicability is limited by its computational inefficiency. To address this limitation, we propose a memorization-based framework that accelerates Anchors while preserving explanation fidelity and interpretability. Our approach leverages the iterative nature of Anchors' algorithm which gradually refines an explanation until it is precise enough for a given input by storing and reusing intermediate results obtained during prior explanations. Specifically, we maintain a memory of low-precision, high-coverage rules and introduce a rule transformation framework to adapt them to new inputs: the horizontal transformation adapts a pre-trained explanation to the current input by replacing features, and the vertical transformation refines the general explanation until it is precise enough for the input. We evaluate our method across tabular, text, and image datasets, demonstrating that it significantly reduces explanation generation time while maintaining fidelity and interpretability, thereby enabling the practical adoption of Anchors in time-sensitive applications.
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