100 citations · 175 across the 12 of their papers we have counts for
5 papers · 1 filter
Learning from missing data with the Latent Block Model
Gabriel Frisch, Jean-Benoist Léger, Yves Grandvalet
Missing data can be informative. Ignoring this information can lead to misleading conclusions when the data model does not allow information to be extracted from the missing data.…
Representation Transfer by Optimal Transport
Xuhong Li, Yves Grandvalet, Rémi Flamary +2
Learning generic representations with deep networks requires massive training samples and significant computer resources. To learn a new specific task, an important issue is to tra…
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Xuhong Li, Yves Grandvalet, Franck Davoine
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch. When using fine-tuning, the underlying assumption is…
Theory of Optimizing Pseudolinear Performance Measures: Application to F-measure
Shameem A Puthiya Parambath, Nicolas Usunier, Yves Grandvalet
Non-linear performance measures are widely used for the evaluation of learning algorithms. For example, -measure is a commonly used performance measure for classification proble…
An Efficient Approach to Sparse Linear Discriminant Analysis
Luis Francisco Sanchez Merchante, Yves Grandvalet, Gerrad Govaert
We present a novel approach to the formulation and the resolution of sparse Linear Discriminant Analysis (LDA). Our proposal, is based on penalized Optimal Scoring. It has an exact…