International audienceThe C-bound, introduced in Lacasse et al., gives a tight upper bound on the risk of a binary majority vote classifier. In this work, we present a first step towards extending this work to more complex outputs, by providing generalizations of the C-bound to the multiclass and multi-label settings
The published version is available here: http://link.springer.com/article/10.1007/s10994-014-5462-zI...
We study the generalisation properties of majority voting on finite ensembles of classifiers, provin...
Majority voting is often employed as a tool to increase the robustness of data-driven decisions and ...
International audienceThe C-bound, introduced in Lacasse et al., gives a tight upper bound on the ri...
PAC-Bayesian theory provides generalization bounds for weighted majority vote. However, these bounds...
This paper generalizes an important result from the PAC-Bayesian literature for binary classificatio...
International audienceThis paper generalizes a pivotal result from the PAC-Bayesian literature —the ...
International audienceIn this paper, we propose a transductive bound over the risk of the majority v...
In this paper, we propose a transductive bound over the risk of the majority vote classifier learned...
International audienceIn the PAC-Bayesian literature, the C-Bound refers to an insightful relation b...
We tackle the PAC-Bayesian Domain Adaptation (DA) problem. This arrives when one desires to learn, f...
International audienceWe investigate a stochastic counterpart of majority votes over finite ensemble...
The published version is available here: http://link.springer.com/article/10.1007/s10994-014-5462-zI...
We study the generalisation properties of majority voting on finite ensembles of classifiers, provin...
Majority voting is often employed as a tool to increase the robustness of data-driven decisions and ...
International audienceThe C-bound, introduced in Lacasse et al., gives a tight upper bound on the ri...
PAC-Bayesian theory provides generalization bounds for weighted majority vote. However, these bounds...
This paper generalizes an important result from the PAC-Bayesian literature for binary classificatio...
International audienceThis paper generalizes a pivotal result from the PAC-Bayesian literature —the ...
International audienceIn this paper, we propose a transductive bound over the risk of the majority v...
In this paper, we propose a transductive bound over the risk of the majority vote classifier learned...
International audienceIn the PAC-Bayesian literature, the C-Bound refers to an insightful relation b...
We tackle the PAC-Bayesian Domain Adaptation (DA) problem. This arrives when one desires to learn, f...
International audienceWe investigate a stochastic counterpart of majority votes over finite ensemble...
The published version is available here: http://link.springer.com/article/10.1007/s10994-014-5462-zI...
We study the generalisation properties of majority voting on finite ensembles of classifiers, provin...
Majority voting is often employed as a tool to increase the robustness of data-driven decisions and ...