Online Mean Field Approximation for Automated Experimentation

In this paper, we propose a semi-supervised online graph labelling method that a↵ords early learning capability. We use mean field approximation for predicting the unknown labels of the vertices of the graph with high accuracy on the standard benchmark datasets. The minimum cut is the energy function of our probabilistic model that encodes the uncertainty about the labels of the vertices. Our method shows that it can learn early given any choice of experiments that may take place in the automated experimentation systems used for scientific discovery.