Recently, an increasing number of laws have governed the useability of users' privacy. For
example, Article 17 of the General Data Protection Regulation (GDPR), the right to be …

Unlearning the data observed during the training of a machine learning (ML) model is an
important task that can play a pivotal role in fortifying the privacy and security of ML-based …

Influence functions approximate the effect of training samples in test-time predictions and
have a wide variety of applications in machine learning interpretability and uncertainty …

The deployment of machine learning (ML) and statistical models is beginning to transform
the practice of healthcare, with models now able to help clinicians diagnose conditions like …

Deleting data from a trained machine learning (ML) model is a critical task in many
applications. For example, we may want to remove the influence of training points that might …

We show that the influence of a subset of the training samples can be removed--or"
forgotten"--from the weights of a network trained on large-scale image classification tasks …

Influence functions estimate the effect of removing a training point on a model without the
need to retrain. They are based on a first-order Taylor approximation that is guaranteed to …

With the fast development of algorithmic governance, fairness has become a compulsory
property for machine learning models to suppress unintentional discrimination. In this paper …

We describe a procedure for removing dependency on a cohort of training data from a
trained deep network that improves upon and generalizes previous methods to different …

Leakage of data from publicly available Machine Learning (ML) models is an area of
growing significance since commercial and government applications of ML can draw on …