4 citations · 6 across the 7 of their papers we have counts for
7 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…
Unbiased Approximate Vector-Jacobian Products for Efficient Backpropagation
Killian Bakong, Laurent Massoulié, Edouard Oyallon +1
In this work we introduce methods to reduce the computational and memory costs of training deep neural networks. Our approach consists in replacing exact vector-jacobian products b…
Adaptive collaboration for online personalized distributed learning with heterogeneous clients
Constantin Philippenko, Batiste Le Bars, Kevin Scaman +1
We study the problem of online personalized decentralized learning with statistically heterogeneous clients collaborating to accelerate local training. An important challenge i…
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…
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles
Kevin Scaman, Mathieu Even, Batiste Le Bars +1
In this paper, our aim is to analyse the generalization capabilities of first-order methods for statistical learning in multiple, different yet related, scenarios including supervi…
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
Batiste Le Bars, Aurélien Bellet, Marc Tommasi +2
This paper presents a new generalization error analysis for Decentralized Stochastic Gradient Descent (D-SGD) based on algorithmic stability. The obtained results overhaul a series…
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…