6 citations · 8 across the 7 of their papers we have counts for
8 papers
Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness
Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1
Recent work has established a fundamental trilemma between Byzantine robustness, local differential privacy (LDP), and optimization error in distributed learning. We show that this…
Disparate Impact in Synthetic Data Generation
Paul Andrey, Michaël Perrot, Batiste Le Bars +1
We revisit the fairness notion of disparate impact for synthetic data generation (SDG), that assesses whether the utility of generated records is the same across sensitive groups.…
Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning
Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1
Robust distributed learning algorithms aim to maintain reliable performance despite the presence of misbehaving workers. Such misbehaviors are commonly modeled as \textit{Byzantine…
Marginal and training-conditional guarantees in one-shot federated conformal prediction
Pierre Humbert, Batiste Le Bars, Aurélien Bellet +1
We study conformal prediction in the one-shot federated learning setting. The main goal is to compute marginally and training-conditionally valid prediction sets, at the server-lev…
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…
One-Shot Federated Conformal Prediction
Pierre Humbert, Batiste Le Bars, Aurélien Bellet +1
In this paper, we introduce a conformal prediction method to construct prediction sets in a oneshot federated learning setting. More specifically, we define a quantile-of-quantiles…
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data
Batiste Le Bars, Aurélien Bellet, Marc Tommasi +2
One of the key challenges in decentralized and federated learning is to design algorithms that efficiently deal with highly heterogeneous data distributions across agents. In this…