提出无需重训练的评估方法，判断模型是否真正遗忘部分数据。
Unlearning Evaluation through Subset Statistical Independence
- 基于希尔伯特-施密特独立性准则，检测模型对特定数据子集的输出依赖性。
- 在不依赖训练配置或标签的情况下，准确区分已训练与未训练数据子集。
- 适合评估真实场景中模型遗忘效果，尤其适用于无法获取原始训练信息的场景。
机器学习中的模型遗忘评估仍具挑战性，现有方法通常需重新训练参考模型或进行成员推断攻击，均依赖训练配置或标注标签，在实际场景中难以应用。鉴于多数遗忘算法仅移除少量随机训练数据，我们提出一种基于统计独立性的子集级评估框架。具体地，利用希尔伯特-施密特独立性准则（Hilbert-Schmidt Independence Criterion）检测模型在给定子集上的输出是否具有统计依赖性，无需重训练或辅助分类器。该方法提供了一种简单、独立的评估流程，契合遗忘工作流。大量实验表明，该方法能可靠区分训练内与训练外子集，并清晰识别遗忘效果，即便现有评估方法失效时依然有效。
原文摘要 · Abstract (English)
Evaluating machine unlearning remains challenging, as existing methods typically require retraining reference models or performing membership inference attacks, both of which rely on prior access to training configuration or supervision labels, making them impractical in realistic scenarios. Motivated by the fact that most unlearning algorithms remove a small, random subset of the training data, we propose a subset-level evaluation framework based on statistical independence. Specifically, we design a tailored use of the Hilbert-Schmidt Independence Criterion to assess whether the model outputs on a given subset exhibit statistical dependence, without requiring model retraining or auxiliary classifiers. Our method provides a simple, standalone evaluation procedure that aligns with unlearning workflows. Extensive experiments demonstrate that our approach reliably distinguishes in-training from out-of-training subsets and clearly differentiates unlearning effectiveness, even when existing evaluations fall short.
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