Mathématiques et Informatique Appliquées - Paris-Saclay (MIA)

Présentation du laboratoire

Le laboratoire Mathématiques et Informatique Appliquées est une unité dont les recherches portent sur la modélisation et l'apprentissage statistique et informatique. Les trois équipes, Statistique et génome, MORSE et LInK, possèdent des compétences en méthodes d'inférences statistiques et en algorithmiques, qu'elles appliquent aux domaines de la biologie, l'écologie, l'environnement, l'agronomie et l'agro-alimentaire.

Les 20 dernières publications

Titre Auteurs Date de publication Source
Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles Alizarine Lorenzi, Cyril Bauland, Sophie Pin, Delphine Madur, V. Combes, Tristan Mary-Huard, Alain Charcosset, Laurence Moreau 01/03/2024 Theoretical and Applied Genetics
Social network analysis: Which contributions to the analysis of agricultural systems resilience? S. Ouadah 01/03/2024 Agricultural Systems
Corrigendum to “Learning constraints through partial queries” [Artificial Intelligence 319 (2023) 103896](S0004370223000425)(10.1016/j.artint.2023.103896) George Katsirelos 01/03/2024 Artificial Intelligence
Some thoughts about transfer learning. What role for the source domain? A. Cornuéjols 01/03/2024 International Journal of Approximate Reasoning
A brief introduction to nature-inspired computing, optimization, and applications Alberto Tonda 01/01/2024 Advances in Computers
Reprint of: Some thoughts about transfer learning. What role for the source domain? A. Cornuéjols 01/01/2024 International Journal of Approximate Reasoning
Scaling by subsampling for big data, with applications to statistical learning Jessica Tressou 01/01/2024 Journal of Nonparametric Statistics
Retrieving Soil Moisture from Sentinel-1: Limitations over Certain Crops and Sensitivity to the First Soil Thin Layer Hassan Bazzi 01/01/2024 Water (Switzerland)
Veni, Vidi, Evolvi commentary on W. B. Langdon’s “Jaws 30” Alberto Tonda 01/12/2023 Genetic Programming and Evolvable Machines
Biquality learning: a framework to design algorithms dealing with closed-set distribution shifts Pierre Nodet, A. Cornuéjols 01/12/2023 Machine Learning
Knowledge coproduction to improve assessments of nature's contributions to people Améline Vallet 01/12/2023 Conservation Biology
Efficient Bayesian automatic calibration of a functional- structural wheat model using an adaptive design and a metamodelling approach Emmanuelle Blanc, Jerome Enjalbert, Timothée Flutre 21/11/2023 Journal of Experimental Botany
A robust mRNA signature obtained via recursive ensemble feature selection predicts the responsiveness of omalizumab in moderate-to-severe asthma Alberto Tonda 01/11/2023 Clinical and Translational Allergy
Automated calibration for stability selection in penalised regression and graphical models Julien Chiquet 01/11/2023 Journal of the Royal Statistical Society. Series C: Applied Statistics
Crop biocultural traits shape seed networks: Implications for social-ecological resilience in south eastern Senegal S. Ouadah 01/10/2023 Agricultural Systems
Understanding How In-Visualization Provenance Can Support Trade-off Analysis Mehdi Chakhchoukh, Nadia Boukhelifa 01/09/2023 IEEE Transactions on Visualization and Computer Graphics
Forecasting Pathogen Dynamics with Bayesian Model-Averaging: Application to Xylella fastidiosa Éric Parent 01/07/2023 Bulletin of Mathematical Biology
Detecting directional and non-directional epistasis in bi-parental populations using genomic data Tristan Mary-Huard 01/07/2023 Genetics
Simple random forest classification algorithms for predicting occurrences and sizes of wildfires David Makowski 01/06/2023 Extremes
Scalable clustering of segmented trajectories within a continuous time framework: application to maritime traffic data Laetitia Chapel, Chloé Friguet, Romain Tavenard 01/06/2023 Machine Learning

Nombre de publications du laboratoire par domaine scientifique (2016-2021)

Chaque publication du laboratoire peut être rangée dans une ou plusieurs disciplines scientifiques : la figure ci-dessus présente le nombre de publications du laboratoire pour chaque discipline de la classification ASJC (Elsevier)