@Article{AdrienPavao2023,
author="Adrien Pavao
and Isabelle Guyon
and Anne-Catherine Letournel
and Dinh-Tuan Tran
and Xavier Baro
and Hugo Jair Escalante
and Sergio Escalera
and Tyler Thomas
and Zhen Xu",
title="CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges",
journal="Journal of Machine Learning Research",
year="2023",
abstract="CodaLab Competitions is an open source web platform designed to help data scientists and research teams to crowd-source the resolution of machine learning problems through the organization of competitions, also called challenges or contests. CodaLab Competitions provides useful features such as multiple phases, results and code submissions, multi-score leaderboards, and jobs runninginside Docker containers. The platform is very flexible and can handle large scale experiments, by allowing organizers to upload large datasets and provide their own CPU or GPU compute workers.",
optnote="HUPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3973), last updated on Fri, 26 Jan 2024 15:10:22 +0100",
opturl="https://www.jmlr.org/"
}