39 citations · 95 across the 35 of their papers we have counts for
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cs.LG2018★ 1 cited
Learning from graphs with structural variation
Rune Kok Nielsen, Andreas Nugaard Holm, Aasa Feragen
We study the effect of structural variation in graph data on the predictive performance of graph kernels. To this end, we introduce a novel, noise-robust adaptation of the GraphHop…
stat.ML2018★ 5 cited
Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models
Anton Mallasto, Søren Hauberg, Aasa Feragen
Latent variable models (LVMs) learn probabilistic models of data manifolds lying in an \emph{ambient} Euclidean space. In a number of applications, a priori known spatial constrain…