Concept
Loi d'extremum généralisée
Publications associées (32)
A story of two transitions: From adhesive to abrasive wear and from ductile to brittle regime
Jean-François Molinari, Sacha Zenon Wattel
Atomistic simulations performed with a family of model potential with tunable hardness have proven to be a great tool for advancing the understanding of wear processes at the asperity level. They have been instrumental in finding a critical length scale, w ...
2024Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data
Michael Christoph Gastpar, Aditya Pradeep
One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...
2023Scalable and Privacy-Preserving Federated Principal Component Analysis
Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, David Jules Froelicher, Apostolos Pyrgelis, Joao André Gomes de Sá e Sousa, Jean-Philippe Léonard Bossuat
Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...
IEEE COMPUTER SOC2023Adversarial Parametric Pose Prior
Pascal Fua, Mathieu Salzmann, Anastasia Remizova, Victor Constantin, Sina Honari, Andrey Davydov
The Skinned Multi-Person Linear (SMPL) model represents human bodies by mapping pose and shape parameters to body meshes. However, not all pose and shape parameter values yield physically-plausible or even realistic body meshes. In other words, SMPL is und ...
IEEE2022Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Volkan Cevher, Paul Thierry Yves Rolland
This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models. Using score matching algorithms as a building block, we show how to design a new generation of scalable causal disc ...
2022Dalton: Learned Partitioning for Distributed Data Streams
Anastasia Ailamaki, Eleni Zapridou, Ioannis Mytilinis
To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield imbalanced load assignments and create stragglers that hinder scalability. Deciding on a static partitioning for a ...
2022Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data
Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi
Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...
JMLR-JOURNAL MACHINE LEARNING RESEARCH2022Flood of 23 November 2019 on the French Riviera: a comparison of discharges estimated by image analysis and hydrological modelling
Gauthier Paul Daniel Marie Rousseau, Etienne Robert
The short and heavy rainfall events observed on the French Riviera often lead to flash floods on coastal catchments: during the flood of 2 October 2015, a peak discharge between 185 and 295 m(3)/s was estimated on the Brague at Biot at 8:00 PM, whereas the ...
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD2022Feature distribution learning by passive exposure
Humans can rapidly estimate the statistical properties of groups of stimuli, including their average and variability. But recent studies of so-called Feature Distribution Learning (FDL) have shown that observers can quickly learn even more complex aspects ...
ELSEVIER2022