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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 comprehensive survey of forgetting in deep learning beyond continual learning
Forgetting refers to the loss or deterioration of previously acquired knowledge. While
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …
existing surveys on forgetting have primarily focused on continual learning, forgetting is a …
Machine unlearning: Solutions and challenges
Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …
data, posing risks of privacy breaches, security vulnerabilities, and performance …
Continual forgetting for pre-trained vision models
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 …
vision models is becoming evident nowadays. In real-world scenarios erasure requests …
Machine unlearning: Taxonomy, metrics, applications, challenges, and prospects
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 …
regulations to protect data privacy. Data users have been endowed with the “right to be …
Certified minimax unlearning with generalization rates and deletion capacity
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 …
models. Most of the existing works focus on unlearning from standard statistical learning …
Inexact unlearning needs more careful evaluations to avoid a false sense of privacy
The high cost of model training makes it increasingly desirable to develop techniques for
unlearning. These techniques seek to remove the influence of a training example without …
unlearning. These techniques seek to remove the influence of a training example without …
Ultrare: Enhancing receraser for recommendation unlearning via error decomposition
With growing concerns regarding privacy in machine learning models, regulations have
committed to granting individuals the right to be forgotten while mandating companies to …
committed to granting individuals the right to be forgotten while mandating companies to …
Towards efficient and effective unlearning of large language models for recommendation
Conclusion In this letter, we propose E2URec, the efficient and effective unlearning method
for LLMRec. Our method enables LLMRec to efficiently forget the specific data by only …
for LLMRec. Our method enables LLMRec to efficiently forget the specific data by only …
Safe: Machine unlearning with shard graphs
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 …
models on a diverse collection of data while minimizing the expected cost to remove the …