提出简单高效方法生成稀疏且合理的反事实解释。
S-CFE: Simple Counterfactual Explanations
- 用加速近端梯度法处理非凸优化，支持多种模型和可解释性度量。
- 生成的解释更短且贴近真实数据分布，计算效率高。
- 适合需要可行动解释的场景，如医疗、金融决策透明化。
我们研究分类器的最优稀疏、流形对齐反事实解释问题。传统上，这被建模为包含多个非凸项（如分类损失函数和流形对齐度量）的优化问题。引入稀疏性约束进一步增加了复杂性。现有方法通常针对特定模型和可解释性度量，依赖凸的ℓ₁正则化来实现稀疏性。本文采用加速近端梯度（APG）方法，这是一种简单高效的梯度一阶算法，能处理光滑非凸目标函数和非光滑ℓₚ（0≤p<1）正则化。该方法可无缝集成多种分类器和可解释性度量，同时生成更稀疏的解。算法仅需可微的数据流形正则项，并支持特征范围的盒约束，确保生成的反事实解释具有可操作性。在真实数据集上的实验表明，本方法能有效生成稀疏、流形对齐的反事实解释，同时保持与事实数据的接近性和计算效率。
原文摘要 · Abstract (English)
We study the problem of finding optimal sparse, manifold-aligned counterfactual explanations for classifiers. Canonically, this can be formulated as an optimization problem with multiple non-convex components, including classifier loss functions and manifold alignment (or \emph{plausibility}) metrics. The added complexity of enforcing \emph{sparsity}, or shorter explanations, complicates the problem further. Existing methods often focus on specific models and plausibility measures, relying on convex $\ell_1$ regularizers to enforce sparsity. In this paper, we tackle the canonical formulation using the accelerated proximal gradient (APG) method, a simple yet efficient first-order procedure capable of handling smooth non-convex objectives and non-smooth $\ell_p$ (where $0 \leq p < 1$) regularizers. This enables our approach to seamlessly incorporate various classifiers and plausibility measures while producing sparser solutions. Our algorithm only requires differentiable data-manifold regularizers and supports box constraints for bounded feature ranges, ensuring the generated counterfactuals remain \emph{actionable}. Finally, experiments on real-world datasets demonstrate that our approach effectively produces sparse, manifold-aligned counterfactual explanations while maintaining proximity to the factual data and computational efficiency.
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