Probabilistic End-to-End Graph-based Semi-Supervised Learning

Mariana Vargas Vieyra (1) , Aurélien Bellet (1) , Pascal Denis (1)

1 MAGNET - Machine Learning in Information Networks

In this paper we address the problem of graph-based semi-supervised learning in tasks where a graph describing the relationships between data points is not available. We propose a method to jointly learn the graph and the parameters of a semi-supervised model using a probabilistic framework. We empirically show our proposal achieves competitive results in a variety of datasets.

Domaines: Intelligence artificielle [cs.AI]

Mariana Vargas Vieyra, Aurélien Bellet, Pascal Denis. Probabilistic End-to-End Graph-based Semi-Supervised Learning. Graph Representation Learning workshop, NeurIPS, 2019, Vancouver, Canada. ⟨hal-03501846⟩