Machine learning practitioners are often faced with a choice between a discriminative and a generative approach to modelling. Here, we present a model based on a hybrid approach that breaks down some of the barriers between the discriminative and generative points of view, allowing continuous dimensionality reduction of hybrid discretecontinuous data, discriminative classication with missing inputs and manifold learning informed by class labels.
['Ricardo Andrade Pacheco', 'James Hensman', 'Max Zwiessele', 'Neil D. Lawrence']
{Hybrid Discriminative-Generative Approach with Gaussian Processes}