Invited speacker: Devis Tuia (École Polytechnique Fédérale de Lausanne, Switzerland), Machine learning for the environment: monitoring the pulse of our Planet with remotely sensed data
Romain Tavenard, Johann Faouzi, Yann Cabanes: Apprentissage automatique pour les séries temporelles
Baptiste Lafabregue, Antoine Cornuéjols, Pierre Gançarski: Approches par ensembles
Céline Hudelot, Wassila Ouerdane, Jean-Philippe Poli: Initiation à l’IA explicable
CAp (from July 3 to July 5) is an interdisciplinary gathering of researchers at the intersection of machine learning, applied mathematics, and related areas.
The submission website can be found here.
Submitted papers can be either in English or in French and we encourage two types of submissions:
Full research papers on the theme of machine learning theory and its applications should not exceed 10 pages in CAp double-column format (including references and figures). A suitable LaTeX template for CAp is available here.
Short papers can be up to 6 pages using the same format as the full papers. They present original ideas and provide an opportunity to describe significant work in progress.
We also encourage the submission of recent (2022 or 2023) papers accepted to high level conferences and journals in machine learning. These papers will also be reviewed (lightly) by the program committee. If accepted, they will be presented at the conference but will not appear in any (online) proceedings. Note that, in this particular case, the paper can be submitted in the original conference format (length and style) and the reviews given by the conference/ML journal where it was accepted should be included as the first pages of the submission in addition to a link to the corresponding conference/ML journal web page. The submission of the reviews and the original paper should be merged and submitted into a single PDF file on the easychair website.
Some accepted papers will be presented in a long (20 minutes) oral presentation and all the accepted papers will be given the opportunity to be presented as a spotlight (3 minutes) and as a poster at the conference. These presentations are an opportunity to have constructive and rigorous feedbacks, as well as to establish contacts with members of the french machine learning community. PhD Students are particularly welcome and encouraged to submit papers. Contributions will be freely distributed on the conference website, subject to approval by the authors.
The conference and program chairs of CAp 2023 invite those working in areas related to any aspect of machine learning to submit original papers for review. Solicited topics include, but are not limited to:
Learning theory, models and paradigms:
Multi-target, multi-task, multi-instance, multi-view and transfer learning
Supervised, unsupervised and semi-supervised learning
Matrix and tensor factorization
Optimal Transport for Machine Learning
Privacy preserving Machine Learning
Ethic and fairness of Machine Learning
Interpretable Machine Learning
Ensemble learning and boosting
Neural networks and deep learning
Optimization et related problems:
Large-scale machine learning and optimization
Machine learning and structured data (spatio-temporal data, tree, graph)