Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Bars, Batiste Le"'
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-level, in only one round of communication between the agents and the serv
Externí odkaz:
http://arxiv.org/abs/2405.12567
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 supervised learning, transfer learning, robust learning and federated learnin
Externí odkaz:
http://arxiv.org/abs/2307.04679
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 of recent works that suggested an increased instability due to decent
Externí odkaz:
http://arxiv.org/abs/2306.02939
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 estimator and prove that for any distribution, it is possible to outp
Externí odkaz:
http://arxiv.org/abs/2302.06322
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 paper, we revisit the analysis of the popular Decentralized Stochastic
Externí odkaz:
http://arxiv.org/abs/2204.04452
In this paper, we introduce a robust nonparametric density estimator combining the popular Kernel Density Estimation method and the Median-of-Means principle (MoM-KDE). This estimator is shown to achieve robustness to any kind of anomalous data, even
Externí odkaz:
http://arxiv.org/abs/2006.16590
This work focuses on the estimation of multiple change-points in a time-varying Ising model that evolves piece-wise constantly. The aim is to identify both the moments at which significant changes occur in the Ising model, as well as the underlying g
Externí odkaz:
http://arxiv.org/abs/1910.08512
Autor:
Bars, Batiste Le, Kalogeratos, Argyris
Publikováno v:
IEEE International Conference on Computer Communications 2019 (INFOCOM), 2019
In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks. The signal of the communication activity is modeled by means of a clique stream: each occurring communication event is in
Externí odkaz:
http://arxiv.org/abs/1902.04521
This paper presents a new generalization error analysis for the Decentralized Stochastic Gradient Descent (D-SGD) algorithm based on algorithmic stability. The obtained results largely improve upon state-of-the-art results, and even invalidate their
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6420d349261303194539a2d292e6764