4 citations · 5 across the 4 of their papers we have counts for
4 papers
Variance-Reduced Unlearning using Forget Set Gradients
Martin Van Waerebeke, Marco Lorenzi, Kevin Scaman +2
In machine unlearning, unlearning is a popular framework that provides formal guarantees on the effectiveness of the removal of a subset of training data, the for…
When to Forget? Complexity Trade-offs in Machine Unlearning
Martin Van Waerebeke, Marco Lorenzi, Giovanni Neglia +1
Machine Unlearning (MU) aims at removing the influence of specific data points from a trained model, striving to achieve this at a fraction of the cost of full model retraining. In…
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
Yann Fraboni, Martin Van Waerebeke, Kevin Scaman +3
Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure. Federate…
On the pitfalls of entropy-based uncertainty for multi-class semi-supervised segmentation
Martin Van Waerebeke, Gregory Lodygensky, Jose Dolz
Semi-supervised learning has emerged as an appealing strategy to train deep models with limited supervision. Most prior literature under this learning paradigm resorts to dual-base…