In light of recent privacy regulations, machine unlearning has attracted significant attention in the research community. However, current studies predominantly assess the overall success of unlearning approaches, overlooking the varying difficulty of unlearning individual training samples. As a result, the broader feasibility of machine unlearning remains under-explored. This paper presents a set of novel metrics for quantifying the difficulty of unlearning by jointly considering the properties of target model and data distribution. Specifically, we propose several heuristics to assess the conditions necessary for a successful unlearning operation, examine the variations in unlearning difficulty across different training samples, and present a ranking mechanism to identify the most challenging samples to unlearn. We highlight the effectiveness of the Kernelized Stein Discrepancy (KSD), a parameterized kernel function tailored to each model and dataset, as a heuristic for evaluating unlearning difficulty. Our approach is validated through multiple classification tasks and established machine unlearning algorithms, demonstrating the practical feasibility of unlearning operations across diverse scenarios.

本研究解决了当前机器忘记领域中对不同训练样本忘记难度评估不足的问题。通过提出新的度量标准和启发式方法，本文探讨了影响机器忘记成功的条件，并建立了样本忘记难度的排名机制。研究结果表明，使用针对特定模型和数据集的核化斯坦因差异性（KSD）作为评估工具，有助于提高机器忘记操作的有效性和可行性。

关于机器忘记可行性的理解