Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

We study the problem of unlearning datapoints from a learnt model. The learner first
receives a dataset $ S $ drawn iid from an unknown distribution, and outputs a model …

Responding to user data deletion requests, removing noisy examples, or deleting corrupted
training data are just a few reasons for wanting to delete instances from a machine learning …

Language models (LMs) are trained on vast amounts of text data, which may include private
and copyrighted content. Data owners may request the removal of their data from a trained …

Personal digital data is a critical asset, and governments worldwide have enforced laws and
regulations to protect data privacy. Data users have been endowed with the right to be …

Humans and animals have a natural ability to quickly adapt to their surroundings, but
machine-learning models, when subjected to changes, often require a complete retraining …

In this paper, we present an incremental variant of the Twin Support Vector Machine
(TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with …

The widespread use of machine learning models, coupled with large datasets and
increasingly complex models have led to a general lack of understanding for how individual …

We consider the learning–unlearning paradigm defined as follows. First given a dataset, the
goal is to learn a good predictor, such as one minimizing a certain loss. Subsequently, given …

Machine unlearning algorithms, designed for selective removal of training data from models,
have emerged as a promising approach to growing privacy concerns. In this work, we …