Yang et al. (2023) discovered that removing a mere 1% of training points can often lead to the flipping of a prediction. Given the prevalence of noisy data in machine learning models, we pose the question: can we also result in the flipping of a test prediction by relabeling a small subset of the training data before the model is trained? In this paper, utilizing the extended influence function, we propose an efficient procedure for identifying and relabeling such a subset, demonstrating consistent success. This mechanism serves multiple purposes: (1) providing a complementary approach to challenge model predictions by recovering potentially mislabeled training points; (2) evaluating model resilience, as our research uncovers a significant relationship between the subset's size and the ratio of noisy data in the training set; and (3) offering insights into bias within the training set. To the best of our knowledge, this work represents the first investigation into the problem of identifying and relabeling the minimal training subset required to flip a given prediction.

翻译：Yang等人（2023）发现，移除仅1%的训练点通常就能导致预测翻转。鉴于机器学习模型中噪声数据的普遍性，我们提出一个问题：是否可以通过在模型训练前重新标记一小部分训练数据，来实现测试预测的翻转？在本文中，利用扩展影响函数，我们提出了一种高效的程序来识别并重新标记这样的子集，并展示了持续的成功。这一机制服务于多个目的：（1）通过恢复可能被错误标记的训练点，为挑战模型预测提供一种补充方法；（2）评估模型鲁棒性，因为我们的研究揭示了子集大小与训练集中噪声数据比例之间的显著关系；（3）提供对训练集中偏差的洞察。据我们所知，这项工作是首次对识别和重新标记最小训练子集以翻转给定预测的问题进行的探究。