3 papers
stat.ML2022
Functional Output Regression with Infimal Convolution: Exploring the Huber and -insensitive Losses
Alex Lambert, Dimitri Bouche, Zoltan Szabo +1
The focus of the paper is functional output regression (FOR) with convoluted losses. While most existing work consider the square loss setting, we leverage extensions of the Huber…
cs.LG2022
Wind power predictions from nowcasts to 4-hour forecasts: a learning approach with variable selection
Dimitri Bouche, Rémi Flamary, Florence d'Alché-Buc +4
We study short-term prediction of wind speed and wind power (every 10 minutes up to 4 hours ahead). Accurate forecasts for these quantities are crucial to mitigate the negative eff…
stat.ML2020
Nonlinear Functional Output Regression: a Dictionary Approach
Dimitri Bouche, Marianne Clausel, François Roueff +1
To address functional-output regression, we introduce projection learning (PL), a novel dictionary-based approach that learns to predict a function that is expanded on a dictionary…