Advances in Kernel methods for Structured Data (KERMES)

Kernel machines and related Bayesian approaches, such as Gaussian processes, have been widely and successfully used in practice for dealing with such data structures for regression and classification. Kernel methods allow to encode prior knowledge about the data characteristics, to learn the underlying latent functions explaining the data; and allow the combination of different data modalities. The long-term vision of KERMES is tied to open new frontiers and foster research towards new kernel algorithms, a stepping stone before the more ambitious far-end goal of machine reasoning. We aim to learn data structures, to scale methods to work with millions of samples, derive sensible confidence intervals for the predictions, and to advance in causal inference from empirical data. The members of this network are leading experts in kernel methods.

KERMES will promote multidisciplinary collaboration among researchers in the fields of machine learning, signal processing and Bayesian statistics. We aim to create synergies, organize discussion meetings, publish relevant papers, sponsor researcher's mobility, and create a critical mass that allows to launch future scientific projects.