28–29 nov. 2019
Inria Saclay Ile-de-France
Fuseau horaire Europe/Paris

Liste des Contributions

12 sur 12 affichés
Exporter en PDF
  1. Marc Boulle (Orange)
  2. Michele Sebag (CNRS)
  3. David Duvenaud (Harvard University)
    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...
    Aller à la page de la contribution
  4. Matthew Hoffmann (University of Cambridge)
    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...
    Aller à la page de la contribution
  5. Joaquin Vanschoren (Eindhoven University of Technology)
    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...
    Aller à la page de la contribution
  6. Panelists: Marc Boulle, Rich Caruana, David Duvenaud, Matthew Hoffmann, Juergen Schmidhuber, Michèle Sebag, Joaquin Vanschoren.
    Aller à la page de la contribution
  7. 5 spotlights of 2 minutes each
    Aller à la page de la contribution
  8. 9 spotlights of 2 minutes each
    Aller à la page de la contribution

Retour à l'événement