论文标题
评估机器通过认知不确定性学习
Evaluating Machine Unlearning via Epistemic Uncertainty
论文作者
论文摘要
最近，人们对机器的兴趣越来越大，这主要是由于法律要求，例如《通用数据保护法规》（GDPR）和《加利福尼亚州消费者隐私法》。因此，提出了多种方法，以从训练有素的模型中删除特定目标数据点的影响。但是，在评估学习的成功时，当前方法要么使用对抗攻击，要么将其结果与最佳解决方案进行比较，而最佳解决方案通常将重新划分为从头开始。我们认为两种方式在实践中都不足。在这项工作中，我们为基于认知不确定性的机器学习算法提供了评估度量。这是对我们最好的知识进行通用评估指标的第一个定义。
There has been a growing interest in Machine Unlearning recently, primarily due to legal requirements such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act. Thus, multiple approaches were presented to remove the influence of specific target data points from a trained model. However, when evaluating the success of unlearning, current approaches either use adversarial attacks or compare their results to the optimal solution, which usually incorporates retraining from scratch. We argue that both ways are insufficient in practice. In this work, we present an evaluation metric for Machine Unlearning algorithms based on epistemic uncertainty. This is the first definition of a general evaluation metric for Machine Unlearning to our best knowledge.
