让模型用多个解释性专家协同决策，提升准确率与可解释性。
Mixture of Concept Bottleneck Experts
- 引入多个概念专家，动态组合不同函数形式进行预测。
- 线性与符号回归两种变体均显著优于传统CBM，在保持可解释性前提下提升精度。
- 适合需要灵活平衡准确性与人类可理解性的实际应用场景。
概念瓶颈模型（CBMs）通过将预测结果基于人类可理解的概念来提升可解释性。然而，现有CBMs通常将任务预测器限制为单一预设函数形式，制约了预测准确率和对多样化用户需求的适应能力。本文提出混合概念瓶颈专家（M-CBE）框架，从两个维度扩展传统CBMs：一是任务预测器采用的表达式数量（即专家数量），二是每个表达式的函数形式，从而探索此前未被充分研究的设计空间。我们构建了两种新模型：线性M-CBE学习一组有限的线性表达式；符号M-CBE则利用符号回归从数据中发现符合用户指定运算符词汇表的专家函数。实证评估表明，调整专家数量及函数形式能有效在准确率与可解释性之间实现稳健权衡。
原文摘要 · Abstract (English)
Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically constrain their task predictor to a single expression whose functional form is set a priori, limiting both predictive accuracy and adaptability to diverse user needs. We propose Mixture of Concept Bottleneck Experts (M-CBE), a framework that generalizes existing CBMs along two dimensions: the number of expressions, referred to as experts, employed by the task predictor to map concepts to the task, and the functional form each expression takes, thus exposing an underexplored region of this design space. We investigate this region by instantiating two novel models: Linear M-CBE, which learns a finite set of linear expressions, and Symbolic M-CBE, which leverages symbolic regression to discover expert functions from data subject to user-specified operator vocabularies. Empirical evaluation demonstrates that varying the number of expressions and their functional form provides a robust framework for navigating the accuracy-interpretability trade-off.
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