3 citations
- International University of Fundamental StudiesRU3 papers
- Laboratoire de Probabilités, Statistique et ModélisationFR3 papers
- Institute of ArcheologyUA2 papers
- Institut Universitaire de FranceFR2 papers
- Laboratoire de Mathématiques d'OrsayFR2 papers
- Laboratoire de Probabilités et Modèles AléatoiresFR2 papers
- Centre de Recherche en Mathématiques de la DécisionFR1 paper
- Département d'InformatiqueFR1 paper
- École Normale Supérieure - PSLFR1 paper
- Électricité de France (France)FR1 paper
- Institut de FranceFR1 paper
- Institut National Polytechnique de ToulouseFR1 paper
11 papers
Generative Unsupervised Downscaling of Climate Models via Domain Alignment: Application to Wind Fields
Julie Keisler, Boutheina Oueslati, Anastase Charantonis +2
General Circulation Models (GCMs) are widely used for future climate projections, but their coarse spatial resolution and systematic biases limit their direct use for impact studie…
BORWin: Exact algorithm based on a Bi-Objective Relaxation for Window-constrained problems
Christian Artigues, Pascale Bendotti, Alexandre Heintzmann +2
A mixed integer maximization problem involving several additional constraints defined with both a lower and an upper bound is considered. It is assumed that one of such constraints…
SerpentFlow: Generative Unpaired Domain Alignment via Shared-Structure Decomposition
Julie Keisler, Anastase Alexandre Charantonis, Yannig Goude +2
Domain alignment refers broadly to learning correspondences between data distributions from distinct domains. In this work, we focus on a setting where domains share underlying str…
Fast kernel methods: Sobolev, physics-informed, and additive models
Nathan Doumèche, Francis Bach, Gérard Biau +1
Kernel methods are powerful tools in statistical learning, but their cubic complexity in the sample size n limits their use on large-scale datasets. In this work, we introduce a sc…
Forecasting time series with constraints
Nathan Doumèche, Francis Bach, Éloi Bedek +3
Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have in…
Physics-informed kernel learning
Nathan Doumèche, Francis Bach, Gérard Biau +1
Physics-informed machine learning typically integrates physical priors into the learning process by minimizing a loss function that includes both a data-driven term and a partial d…