no code implementations • 13 Jan 2015 • Pascal Germain, Amaury Habrard, Francois Laviolette, Emilie Morvant
This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory.
no code implementations • 13 Jan 2015 • Francois Laviolette, Emilie Morvant, Liva Ralaivola, Jean-Francis Roy
The C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier.
no code implementations • 28 May 2019 • Prudencio Tossou, Basile Dura, Francois Laviolette, Mario Marchand, Alexandre Lacoste
Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods.
no code implementations • 29 Jan 2020 • Patrick Dallaire, Luca Ambrogioni, Ludovic Trottier, Umut Güçlü, Max Hinne, Philippe Giguère, Brahim Chaib-Draa, Marcel van Gerven, Francois Laviolette
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes.