The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey
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 …
neural network architecture is capable of processing graph structured data and bridges the …
A survey of machine unlearning
Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …
abundance of data allows breakthroughs in artificial intelligence, and especially machine …
A review on machine unlearning
H Zhang, T Nakamura, T Isohara, K Sakurai - SN Computer Science, 2023 - Springer
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 …
example, Article 17 of the General Data Protection Regulation (GDPR), the right to be …
Rethinking machine unlearning for large language models
We explore machine unlearning (MU) in the domain of large language models (LLMs),
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …
Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment
Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
Towards unbounded machine unlearning
Deep machine unlearning is the problem of'removing'from a trained neural network a subset
of its training set. This problem is very timely and has many applications, including the key …
of its training set. This problem is very timely and has many applications, including the key …
Salun: Empowering machine unlearning via gradient-based weight saliency in both image classification and generation
With evolving data regulations, machine unlearning (MU) has become an important tool for
fostering trust and safety in today's AI models. However, existing MU methods focusing on …
fostering trust and safety in today's AI models. However, existing MU methods focusing on …
Remember what you want to forget: Algorithms for machine unlearning
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 …
receives a dataset $ S $ drawn iid from an unknown distribution, and outputs a model …
The right to be forgotten in federated learning: An efficient realization with rapid retraining
In Machine Learning, the emergence of the right to be forgotten gave birth to a paradigm
named machine unlearning, which enables data holders to proactively erase their data from …
named machine unlearning, which enables data holders to proactively erase their data from …
In-context unlearning: Language models as few shot unlearners
Machine unlearning, the study of efficiently removing the impact of specific training points on
the trained model, has garnered increased attention of late, driven by the need to comply …
the trained model, has garnered increased attention of late, driven by the need to comply …