In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from th...
Let A be the edge-node incidence matrix of a bipartite graph G = (U, V ; E), I be a subset the nodes of G, and b be a vector such that 2b is integral. We consider the following mi...
For supervised and unsupervised learning, positive definite kernels allow to use large and potentially infinite dimensional feature spaces with a computational cost that only depe...
We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem. In the past, two efficient methods, i.e., Semi-Infinite Linear Prog...
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the ...
Sebastian Gerwinn, Jakob Macke, Matthias Seeger, M...