Martin Van Waerebeke

activity

20222026

most citedSIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization

4 citations · 5 across the 4 of their papers we have counts for

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cs.LG2026

Variance-Reduced $(ε, δ) -$ Unlearning using Forget Set Gradients

Martin Van Waerebeke, Marco Lorenzi, Kevin Scaman +2

In machine unlearning, $(ε, δ) -$ unlearning is a popular framework that provides formal guarantees on the effectiveness of the removal of a subset of training data, the for…

cs.LG2022★ 4 cited

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…

Variance-Reduced (ε,δ)−Unlearning using Forget Set Gradients

SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization

Variance-Reduced $(ε, δ) -$ Unlearning using Forget Set Gradients