2 papers
cs.LG2025
LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification
Thomas Andrews, Mark Law, Sara Ahmadi-Abhari +1
We introduce LearnAD, a neuro-symbolic method for predicting Alzheimer's disease from brain magnetic resonance imaging data, learning fully interpretable rules. LearnAD applies sta…
cs.LG2025
Deep Unlearn: Benchmarking Machine Unlearning for Image Classification
Xavier F. Cadet, Anastasia Borovykh, Mohammad Malekzadeh +2
Machine unlearning (MU) aims to remove the influence of particular data points from the learnable parameters of a trained machine learning model. This is a crucial capability in li…