Rich Caruana
(Microsoft Research)
11/07/2015 08:30
Matthew Hoffmann
(University of Cambridge)
11/07/2015 09:10
Complex optimization and decision making tasks are beginning to play an
increasingly crucial role across a wide variety of scientific fields. This is
becoming more and more evident as entire research programs are being automated.
In this talk I'll describe a set of methods, known as Bayesian optimization,
which provide a very sample efficient approach to this problem. Much of the
gains...
Michele Sebag
(CNRS)
11/07/2015 10:30
Juergen Schmidhuber
(IDSIA)
11/07/2015 14:00
Most machine learning researchers focus on domain-specific learning algorithms. Can we also construct meta-learning algorithms that can learn better learning algorithms, and better ways of learning better learning algorithms, and so on, restricted only by the fundamental limitations of computability? In 1965, J. Good already made informal remarks on an intelligence explosion through such...
David Duvenaud
(Harvard University)
11/07/2015 14:40
How could an artificial intelligence do statistics? It would need an open-ended language of models, and a way to search through and compare those models. Even better would be a system that could explain the different types of structure found, even if that type of structure had never been seen before. This talk presents a prototype of such a system, which builds structured Gaussian processes...
Joaquin Vanschoren
(Eindhoven University of Technology)
11/07/2015 16:30
OpenML is an online machine learning platform where scientists can automatically log and share data sets, code, and experiments, organize them online, and collaborate with researchers all over the world. It helps to automate many tedious aspects of research, is readily integrated into several machine learning tools, and offers easy-to-use APIs. It also enables large-scale and real-time...
11/07/2015 17:30
Panelists: Marc Boulle, Rich Caruana, David Duvenaud, Matthew Hoffmann, Juergen Schmidhuber, Michèle Sebag, Joaquin Vanschoren.
