4 citations · 5 across the 4 of their papers we have counts for
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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…
cs.CV2022★ 1 cited
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…