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Bayesian learningSoftware
machine learningDisease
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providing a self-contained survey on the resulting PAC-Bayes framework and some of its main theoretical and algorithmic developments
This tutorial gives a concise overview of existing PAC-Bayesian theory focusing on three generalizat...
We show that many machine-learning algorithms are specific instances of a single algorithm called th...
International audienceWe exhibit a strong link between frequentist PAC-Bayesian risk bounds and the ...
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their ...
Risk bounds, which are also called generalisation bounds in the statistical learning literature, are...
This paper argues that Bayesian probability theory is a general method for machine learning. From tw...
Institute for Adaptive and Neural ComputationNon-parametric models and techniques enjoy a growing po...
Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, an...
International audienceWe provide a first PAC-Bayesian analysis for domain adaptation (DA) which aris...
This habilitation thesis presents several contributions to (1) the PAC-Bayesian analysis of statisti...
International audienceThis paper provides a theoretical analysis of domain adaptation based on the P...
Die Arbeit beschäftigt sich mit der Kern-basierten überwachten Klassifikation von Mustern im Rahmen ...
All authors contributed equally to this work. We propose a PAC-Bayesian analysis of the transductive...
International audienceWe study the issue of PAC-Bayesian domain adaptation: We want to learn, from a...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
This tutorial gives a concise overview of existing PAC-Bayesian theory focusing on three generalizat...
We show that many machine-learning algorithms are specific instances of a single algorithm called th...
International audienceWe exhibit a strong link between frequentist PAC-Bayesian risk bounds and the ...
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their ...
Risk bounds, which are also called generalisation bounds in the statistical learning literature, are...
This paper argues that Bayesian probability theory is a general method for machine learning. From tw...
Institute for Adaptive and Neural ComputationNon-parametric models and techniques enjoy a growing po...
Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, an...
International audienceWe provide a first PAC-Bayesian analysis for domain adaptation (DA) which aris...
This habilitation thesis presents several contributions to (1) the PAC-Bayesian analysis of statisti...
International audienceThis paper provides a theoretical analysis of domain adaptation based on the P...
Die Arbeit beschäftigt sich mit der Kern-basierten überwachten Klassifikation von Mustern im Rahmen ...
All authors contributed equally to this work. We propose a PAC-Bayesian analysis of the transductive...
International audienceWe study the issue of PAC-Bayesian domain adaptation: We want to learn, from a...
This thesis focuses on sources of error in modern Bayesian analysis and machine learning in the ``bi...
This tutorial gives a concise overview of existing PAC-Bayesian theory focusing on three generalizat...
We show that many machine-learning algorithms are specific instances of a single algorithm called th...
International audienceWe exhibit a strong link between frequentist PAC-Bayesian risk bounds and the ...
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