246 citations · 492 across the 24 of their papers we have counts for
5 papers · 2 filters
A New PAC-Bayesian Perspective on Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette +1
We study the issue of PAC-Bayesian domain adaptation: We want to learn, from a source domain, a majority vote model dedicated to a target one. Our theoretical contribution brings a…
Domain-Adversarial Training of Neural Networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan +5
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is di…
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm
Pascal Germain, Alexandre Lacasse, François Laviolette +2
We propose an extensive analysis of the behavior of majority votes in binary classification. In particular, we introduce a risk bound for majority votes, called the C-bound, that t…
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers
Pascal Germain, Amaury Habrard, François Laviolette +1
In this paper, we provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majorit…
An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context
Pascal Germain, Amaury Habrard, Francois Laviolette +1
This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory. We propose an improvement of the previous domain adaptation bound obtained by Germ…