Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Forgetting refers to the loss or deterioration of previously acquired information or knowledge.
While the existing surveys on forgetting have primarily focused on continual learning …

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …

With growing concerns regarding privacy in machine learning models, regulations have
committed to granting individuals the right to be forgotten while mandating companies to …

In recent years, large language models (LLMs) have spurred a new research paradigm in
natural language processing. Despite their excellent capability in knowledge-based …

Recent regulations concerning the Right to be Forgotten have greatly influenced the
operation of recommender systems, because users now have the right to withdraw their …

Abstract We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large
models on a diverse collection of data while minimizing the expected cost to remove the …

For privacy and security concerns the need to erase unwanted information from pre-trained
vision models is becoming evident nowadays. In real-world scenarios erasure requests …

Regulations now mandate data-driven systems, eg, recommender systems, to empower
users to delete private individual data. This prompts the crucial unlearning of data from …

We study the problem of $(\epsilon,\delta) $-certified machine unlearning for minimax
models. Most of the existing works focus on unlearning from standard statistical learning …