RSS14 Workshop on Non-parametric Learning in Robotics

Overview and goals of the workshop

The growing interest in non-parametric machine learning methods is driven by their flexibility and expressive power on one side and by their efficiency when applied to large data sets on the other side. The latter is particularly interesting for robotic learning tasks, and recent achievements show the potential that these methods can have in practice.
In this workshop, we will present non-parametric learning methods including Gaussian Processes, Spectral Learning, Dirichlet Processes, Deep Learning, and we will show potential applications in robotics.
Renowed experts in the field will present their work, and there will be ample opportunities for interaction and discussion. The aims are to draw further attention of the robotics community to these novel methods, and to highlight their benefits over standard, parametric learning techniques.

Intended audience

Simply, everybody that is interested in marrying robotics with machine learning or is interested in the wide topic of learning from data.
We have arranged a line-up of G R E A T speakers. You must come here!

Authors instructions

Paper Submission: June 6th, 2014
Paper Notification: June 30th, 2014
Go to submission website