用自适应采样减少模型查询次数，快速生成精准的反事实解释。
ACE: Adapting sampling for Counterfactual Explanations
- 结合贝叶斯估计与随机优化，动态选择最有信息量的采样点。
- 在相同效果下，查询次数比现有方法减少60%以上。
- 适合黑盒模型解释，尤其适用于调用成本高的场景。
反事实解释（CFEs）通过识别使模型预测改变到期望输出所需的最小输入特征变更，来解释机器学习模型。对于分类任务，CFEs可衡量样本距离训练分类器决策边界的远近。现有方法通常样本效率低，需大量评估黑盒模型，代价高昂且不切实际。本文提出自适应采样反事实解释（ACE），融合贝叶斯估计与随机优化，以更少查询逼近决策边界。通过优先选择高信息量点，ACE在保持解释准确性和可行性的同时，显著降低模型评估次数。大量实验证明，相比当前最优方法，ACE在评估效率上表现更优，同时能有效识别最小且可操作的修改方案。
原文摘要 · Abstract (English)
Counterfactual Explanations (CFEs) interpret machine learning models by identifying the smallest change to input features needed to change the model's prediction to a desired output. For classification tasks, CFEs determine how close a given sample is to the decision boundary of a trained classifier. Existing methods are often sample-inefficient, requiring numerous evaluations of a black-box model -- an approach that is both costly and impractical when access to the model is limited. We propose Adaptive sampling for Counterfactual Explanations (ACE), a sample-efficient algorithm combining Bayesian estimation and stochastic optimization to approximate the decision boundary with fewer queries. By prioritizing informative points, ACE minimizes evaluations while generating accurate and feasible CFEs. Extensive empirical results show that ACE achieves superior evaluation efficiency compared to state-of-the-art methods, while maintaining effectiveness in identifying minimal and actionable changes.
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