This paper introduces Interpretability-Guided Bi-objective Optimization (IGBO), a framework that trains interpretable models by incorporating structured domain knowledge via a bi-objective formulation. IGBO encodes feature importance hierarchies as a Directed Acyclic Graph (DAG) and uses Temporal Integrated Gradients (TIG) to measure feature importance. To address the Out-of-Distribution (OOD) problem in TIG computation, we propose an Optimal Path Oracle that learns data-manifold-aware integration paths. Theoretical analysis proves convergence properties and robustness to mini-batch noise, while empirical results on time-series data demonstrate IGBO's effectiveness in enforcing DAG constraints with minimal accuracy loss, outperforming standard regularization baselines.

翻译：本文提出可解释性引导的双目标优化框架，该框架通过双目标优化形式融入结构化领域知识来训练可解释模型。IGBO将特征重要性层级编码为有向无环图，并使用时序积分梯度度量特征重要性。为解决TIG计算中的分布外问题，我们提出最优路径预言机来学习数据流形感知的积分路径。理论分析证明了该方法的收敛特性及对小批量噪声的鲁棒性，时序数据上的实证结果表明IGBO能以最小精度损失有效实施DAG约束，其性能优于标准正则化基线方法。