6 citations · 9 across the 11 of their papers we have counts for
5 papers · 2 filters
ConDa: Fast Federated Unlearning with Contribution Dampening
Vikram S Chundawat, Pushkar Niroula, Prasanna Dhungana +3
Federated learning (FL) has enabled collaborative model training across decentralized data sources or clients. While adding new participants to a shared model does not pose great t…
Potion: Towards Poison Unlearning
Stefan Schoepf, Jack Foster, Alexandra Brintrup
Adversarial attacks by malicious actors on machine learning systems, such as introducing poison triggers into training datasets, pose significant risks. The challenge in resolving…
Loss-Free Machine Unlearning
Jack Foster, Stefan Schoepf, Alexandra Brintrup
We present a machine unlearning approach that is both retraining- and label-free. Most existing machine unlearning approaches require a model to be fine-tuned to remove information…
Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening
Stefan Schoepf, Jack Foster, Alexandra Brintrup
Data entry constitutes a fundamental component of the machine learning pipeline, yet it frequently results in the introduction of labelling errors. When a model has been trained on…
An Information Theoretic Approach to Machine Unlearning
Jack Foster, Kyle Fogarty, Stefan Schoepf +3
To comply with AI and data regulations, the need to forget private or copyrighted information from trained machine learning models is increasingly important. The key challenge in u…