39 citations · 83 across the 6 of their papers we have counts for
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cs.LG2021
Unsupervised domain adaptation with non-stochastic missing data
Matthieu Kirchmeyer, Patrick Gallinari, Alain Rakotomamonjy +1
We consider unsupervised domain adaptation (UDA) for classification problems in the presence of missing data in the unlabelled target domain. More precisely, motivated by practical…
cs.LG2013★ 1 cited
The Sum-over-Forests density index: identifying dense regions in a graph
Mathieu Senelle, Silvia Garcia-Diez, Amin Mantrach +3
This work introduces a novel nonparametric density index defined on graphs, the Sum-over-Forests (SoF) density index. It is based on a clear and intuitive idea: high-density region…