Publications associées à Moyenne arithmétique

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, Ivo Fabio Beck, Benjamin Jérémy Laurent Heutte, 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 ...

EPFL Infoscience2023

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

Ekaterina Krymova, Nicola Parolini, Andrea Kraus, David Kraus, Daniel Lopez, 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

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

Julia Schmale, Ivo Fabio Beck, Benjamin Jérémy Laurent Heutte, 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 ...

Pangaea2022

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

Finite Bit Quantization For Decentralized Learning Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif, Marco Carpentiero

In this paper, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained form ...

IEEE2022

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-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Moyenne arithmétique

On the Strategyproofness of the Geometric Median

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

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

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

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

Finite Bit Quantization For Decentralized Learning Under Subspace Constraints

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

Diffusion LMS With Communication Delays: Stability and Performance Analysis

Finite beta effects on short wavelength ion temperature gradient modes

Affine Combination of Diffusion Strategies Over Networks

Graph Chatbot

Chattez avec Graph Search

Diffusion LMS With Communication Delays: Stability and Performance Analysis

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

On the Strategyproofness of the Geometric Median

Finite beta effects on short wavelength ion temperature gradient modes

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

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

Finite Bit Quantization For Decentralized Learning Under Subspace Constraints

Affine Combination of Diffusion Strategies Over Networks

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

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