Concept

Moyenne arithmétique

Publications associées (24)

On the Strategyproofness of the Geometric Median

Rachid Guerraoui, Sadegh Farhadkhani, El Mahdi El Mhamdi, Le Nguyen Hoang

The geometric median, an instrumental component of the secure machine learning toolbox, is known to be effective when robustly aggregating models (or gradients), gathered from potentially malicious (or strategic) users. What is less known is the extent to ...

2023

Aerosol optical absorption coefficients at seven wavelengths in 10 min resolution measured in the Swiss container during MOSAiC 2019/2020

Julia Schmale, Benjamin Jérémy Laurent Heutte, Ivo Fabio Beck, Lubna Dada

This dataset contains aerosol optical absorption coefficients at seven different wavelengths (babs(λ)), averaged to 10 min time resolution, measured during the year-long MOSAiC expedition from October 2019 to September 2020. The measurements were performed ...

Zenodo2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Benjamin Bejar Haro, Ekaterina Krymova, Ntorina Thanou, Nicola Parolini, Daniel Rodrigo Lopez, Andrea Kraus, Daniel Lopez, David Kraus, Markus Scholz, Tao Sun

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

eLIFE SCIENCES PUBL LTD2023

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Michaël Unser, Pakshal Narendra Bohra, Pol del Aguila Pla

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

2023

Equivalent black carbon concentration in 10 minutes time resolution, measured in the Swiss container during MOSAiC 2019/2020

Julia Schmale, Benjamin Jérémy Laurent Heutte, Ivo Fabio Beck, Lubna Dada

This dataset contains equivalent black carbon (eBC) concentrations, averaged to 10 min time resolution, measured during the year-long MOSAiC expedition from October 2019 to September 2020. The measurements were performed in the Swiss container on the D-dec ...

Zenodo2022

Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Zhenyu Zhu

We study the average robustness notion in deep neural networks in (selected) wide and narrow, deep and shallow, as well as lazy and non-lazy training settings. We prove that in the under-parameterized setting, width has a negative effect while it improves ...

2022

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Nirupam Gupta, Shuo Liu

This paper considers the Byzantine fault-tolerance problem in distributed stochastic gradient descent (D-SGD) method - a popular algorithm for distributed multi-agent machine learning. In this problem, each agent samples data points independently from a ce ...

IEEE COMPUTER SOC2021

Diffusion LMS With Communication Delays: Stability and Performance Analysis

Ali H. Sayed, Roula Nassif

We study the problem of distributed estimation over adaptive networks where communication delays exist between nodes. In particular, we investigate the diffusion Least-Mean-Square (LMS) strategy where delayed intermediate estimates (due to the communicatio ...

IEEE Institute of Electrical and Electronics Engineers2020

Finite beta effects on short wavelength ion temperature gradient modes

Laurent Villard

The electromagnetic effect is studied on the short wavelength branch of the ion temperature gradient mode in the linear regime for the first time using a global gyrokinetic model. The short wavelength ion temperature gradient mode growth rate is found to b ...

2020

Affine Combination of Diffusion Strategies Over Networks

Ali H. Sayed, Jie Chen

Diffusion adaptation is a powerful strategy for distributed estimation and learning over networks. Motivated by the concept of combining adaptive filters, this work proposes a combination framework that aggregates the operation of multiple diffusion strate ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Variance-Reduced Stochastic Learning Under Random Reshuffling

Ali H. Sayed, Bicheng Ying, Kun Yuan

Several useful variance-reduced stochastic gradient algorithms, such as SVRG, SAGA, Finito, and SAG, have been proposed to minimize empirical risks with linear convergence properties to the exact minimizer. The existing convergence results assume uniform d ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Random time step probabilistic methods for uncertainty quantification in chaotic and geometric numerical integration

Assyr Abdulle, Giacomo Garegnani

A novel probabilistic numerical method for quantifying the uncertainty induced by the time integration of ordinary differential equations (ODEs) is introduced. Departing from the classical strategy to randomize ODE solvers by adding a random forcing term, ...

2020

Restriction of 3D arithmetic Laplace eigenfunctions to a plane

Riccardo Walter Maffucci

We consider a random Gaussian ensemble of Laplace eigenfunctions on the 3D torus, and investigate the 1-dimensional Hausdorff measure ('length') of nodal intersections against a smooth 2-dimensional toral sub-manifold ('surface'). A prior result of ours pr ...

2020

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

Ali H. Sayed, Roula Nassif

In this article, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs, which can be addressed with diffusion LMS strategies. Most popular grap ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Measurement of b hadron fractions in 13 TeV pp collisions

Olivier Schneider, Aurelio Bay, Yiming Li, Guido Haefeli, Frédéric Blanc, Christoph Frei, Tatsuya Nakada, François Fleuret, Mingkui Wang, Yi Zhang, Michel De Cian, Liang Sun, Luca Pescatore, Ian William Marison, Jorge Miguel Dias Almeida Rodrigues Soares, Elena Graverini, Chitsanu Khurewathanakul, Michael Thompson McCann, Achille Mauri, Renato Quagliani, Valérie Jeanne Thérèse Renaudin-Schouler, Federico Betti, Juan Federico Esteban Müller, Craig Matthew Smith, Aravindhan Venkateswaran, Vitalii Lisovskyi, Katharina Müller, Veronica Sølund Kirsebom, Jian Wang, Zhirui Xu, Lei Zhang, Jessica Prisciandaro, Mark Tobin, Minh Tâm Tran, Niko Neufeld, Matthew Needham, Marc-Olivier Bettler, Greig Alan Cowan, Maurizio Martinelli, Vladislav Balagura, Donal Patrick Hill, Pietro Marino, Mirco Dorigo, Xiaoxue Han, Liupan An, Federico Leo Redi, Plamen Hristov Hopchev, Thibaud Humair, Maxime Schubiger, Hang Yin, Guido Andreassi, Violaine Bellée, Olivier Göran Girard, Axel Kuonen, Preema Rennee Pais, Pavol Stefko, Tara Nanut, Maria Elena Stramaglia, Tommaso Colombo, Vladimir Macko, Guillaume Max Pietrzyk, Evgenii Shmanin, Sebastian Schulte, Dipanwita Dutta, Zheng Wang, Yi Wang, Hans Dijkstra, Gerhard Raven, Peter Clarke, Frédéric Teubert, Giovanni Carboni, Victor Coco, Adam Davis, Paolo Durante, Wenyu Zhang, Yu Zheng, Anton Petrov, Maxim Borisyak, Feng Jiang, Zhipeng Tang

The production fractions of (B) over bar (0)(s) and Lambda(0)(b) hadrons, normalized to the sum of B- and (B) over bar (0) fractions, arc measured in 13 TeV pp collisions using data collected by the LHCb experiment, corresponding to an integrated luminosit ...

American Physical Society2019

Comment on “Origin of water in the Badain Jaran Desert, China: New insight from isotopes” by Wu et al. (2017)

David Andrew Barry, Ho Ling Li, Jiaqi Chen

Precipitation isotope data were used to determine the origin of groundwater in the Badain Jaran Desert (BJD) in the study of Wu et al. (2017). Both precipitation and its isotope composition vary seasonally, so arithmetic averages of precipitation isotope v ...

2018

Mean-Field methods for Structured Deep-Learning in Computer Vision

Pierre Bruno Baqué

In recent years, Machine Learning based Computer Vision techniques made impressive progress. These algorithms proved particularly efficient for image classification or detection of isolated objects. From a probabilistic perspective, these methods can predi ...

EPFL2018

Periodic Splines and Gaussian Processes for the Resolution of Linear Inverse Problems

Michaël Unser, Julien René Pierre Fageot, Anaïs Laure Marie-Thérèse Badoual

This paper deals with the resolution of inverse problems in a periodic setting or, in other terms, the reconstruction of periodic continuous-domain signals from their noisy measurements. We focus on two reconstruction paradigms: variational and statistical ...

2018

The mean-field behavior of processor sharing systems with general job lengths under the SQ(d) policy

Arpan Mukhopadhyay

This paper addresses the mean-field behavior of large-scale systems of parallel servers with a processor sharing service discipline when arrivals are Poisson and jobs have general service time distributions when an SQ(d) routing policy is used. Under this ...

ELSEVIER SCIENCE BV2018

Fault Detection and Faulted Line Identification in Active Distribution Networks Using Synchrophasors-based Real-Time State Estimation

Mario Paolone, Paolo Romano, Marco Pignati, Lorenzo Zanni, Sidi-Rachid Cherkaoui

We intend to prove that PMU-based state estimation processes for active distribution networks exhibit unique time determinism and refresh rate that make them suitable to satisfy the time-critical requirements of protections as well as the accuracy requirem ...

Institute of Electrical and Electronics Engineers2017