5 papers
On the importance of multiple training seeds for evaluating machine unlearning
Jamie Lanyon, Axel Finke, Petros Andreou +1
Machine unlearning aims to remove the influence of certain data points from a trained model without costly retraining. Most practical unlearning algorithms are only approximate and…
RULER: Representation-Level Verification of Machine Unlearning
Georgina Cosma, Axel Finke
Machine unlearning aims to remove the influence of specific training records from a deployed model without retraining from scratch. Current protocols verify this at the output leve…
SUPREME: A Multi-GPU Framework for Reproducible Image Unlearning Method Evaluation
Petros Andreou, Jamie Lanyon, Axel Finke +1
Machine unlearning removes the influence of specific training data from a trained model without retraining it from scratch. Evaluating an unlearning method requires repeating train…
Neural Corrective Machine Unranking
Jingrui Hou, Axel Finke, Georgina Cosma
Machine unlearning in neural information retrieval (IR) systems requires removing specific data whilst maintaining model performance. Applying existing machine unlearning methods t…
Neural Machine Unranking
Jingrui Hou, Axel Finke, Georgina Cosma
We address the problem of machine unlearning in neural information retrieval (IR), introducing a novel task termed Neural Machine UnRanking (NuMuR). This problem is motivated by gr…