555 citations · 585 across the 9 of their papers we have counts for
10 papers
On Monotonicity in AI Alignment
Gilles Bareilles, Julien Fageot, Lê-Nguyên Hoang +4
Comparison-based preference learning has become central to the alignment of AI models with human preferences. However, these methods may behave counterintuitively. After empiricall…
Combining Differential Privacy and Byzantine Resilience in Distributed SGD
Rachid Guerraoui, Nirupam Gupta, Rafael Pinot +2
Privacy and Byzantine resilience (BR) are two crucial requirements of modern-day distributed machine learning. The two concepts have been extensively studied individually but the q…
Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments
Lê-Nguyên Hoang, Louis Faucon, Aidan Jungo +13
Today's large-scale algorithms have become immensely influential, as they recommend and moderate the content that billions of humans are exposed to on a daily basis. They are the d…
Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?
Rachid Guerraoui, Nirupam Gupta, Rafaël Pinot +2
This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Sto…
Garfield: System Support for Byzantine Machine Learning
Rachid Guerraoui, Arsany Guirguis, Jérémy Max Plassmann +2
We present Garfield, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzant…
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)
El-Mahdi El-Mhamdi, Sadegh Farhadkhani, Rachid Guerraoui +3
We study Byzantine collaborative learning, where nodes seek to collectively learn from each others' local data. The data distribution may vary from one node to another. No node…
Distributed Momentum for Byzantine-resilient Learning
El-Mahdi El-Mhamdi, Rachid Guerraoui, Sébastien Rouault
Momentum is a variant of gradient descent that has been proposed for its benefits on convergence. In a distributed setting, momentum can be implemented either at the server or the…
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…
The Hidden Vulnerability of Distributed Learning in Byzantium
El Mahdi El Mhamdi, Rachid Guerraoui, Sébastien Rouault
While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent a…
On The Robustness of a Neural Network
El Mahdi El Mhamdi, Rachid Guerraoui, Sebastien Rouault
With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the \textit{black box} aspect of neural n…