4 citations · 4 across the 3 of their papers we have counts for
3 papers
Sufficient dimension reduction for regression with spatially correlated errors: application to prediction
Liliana Forzani, Rodrigo García Arancibia, Antonella Gieco +2
In this paper, we address the problem of predicting a response variable in the context of both, spatially correlated and high-dimensional data. To reduce the dimensionality of the…
Toward industrial use of continual learning : new metrics proposal for class incremental learning
Konaté Mohamed Abbas, Anne-Françoise Yao, Thierry Chateau +1
In this paper, we investigate continual learning performance metrics used in class incremental learning strategies for continual learning (CL) using some high performing methods. W…
Kernel Inverse Regression for spatial random fields
Jean-Michel Loubes, Anne-Françoise Yao
In this paper, we propose a dimension reduction model for spatially dependent variables. Namely, we investigate an extension of the \emph{inverse regression} method under strong mi…