activity
20172026
most citedThe Hidden Vulnerability of Distributed Learning in Byzantium

555 citations · 603 across the 9 of their papers we have counts for

collaborators

9 papers

cs.LG2026

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…

cs.NE2020

Host-Pathongen Co-evolution Inspired Algorithm Enables Robust GAN Training

Andrei Kucharavy, El Mahdi El Mhamdi, Rachid Guerraoui

Generative adversarial networks (GANs) are pairs of artificial neural networks that are trained one against each other. The outputs from a generator are mixed with the real-world i…

cs.LG2019★ 2 cited

Fast Machine Learning with Byzantine Workers and Servers

El Mahdi El Mhamdi, Rachid Guerraoui, Arsany Guirguis

Machine Learning (ML) solutions are nowadays distributed and are prone to various types of component failures, which can be encompassed in so-called Byzantine behavior. This paper…

cs.DC2019

Genuinely Distributed Byzantine Machine Learning

El-Mahdi El-Mhamdi, Rachid Guerraoui, Arsany Guirguis +2

Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train t…

stat.ML2019

The Probabilistic Fault Tolerance of Neural Networks in the Continuous Limit

El-Mahdi El-Mhamdi, Rachid Guerraoui, Andrei Kucharavy +1

The loss of a few neurons in a brain rarely results in any visible loss of function. However, the insight into what "few" means in this context is unclear. How many random neuron f…

q-bio.PE2018★ 3 cited

Learning to Gather without Communication

El Mahdi El Mhamdi, Rachid Guerraoui, Alexandre Maurer +1

A standard belief on emerging collective behavior is that it emerges from simple individual rules. Most of the mathematical research on such collective behavior starts from imperat…