125 citations
- Centre National de la Recherche ScientifiqueFR30 papers
- Université Jean MonnetFR24 papers
- Lyon 1 UniversitéFR16 papers
- Leading Health CareSE9 papers
- Institut d’Optique Graduate SchoolFR6 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR5 papers
- Institut de Planétologie et d'Astrophysique de GrenobleFR5 papers
- Université Paris-SaclayFR5 papers
- CEA DAM Île-de-FranceFR4 papers
- Centre de Recherche Astrophysique de LyonFR4 papers
- L'Alliance BoviteqCA4 papers
- Télécom ParisFR4 papers
8 papers · 1 filter
PAC-Bayesian Bounds on Constrained f-Entropic Risk Measures
Hind Atbir, Farah Cherfaoui, Guillaume Metzler +2
PAC generalization bounds on the risk, when expressed in terms of the expected loss, are often insufficient to capture imbalances between subgroups in the data. To overcome this li…
Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures
Paul Viallard, Rémi Emonet, Amaury Habrard +2
In statistical learning theory, a generalization bound usually involves a complexity measure imposed by the considered theoretical framework. This limits the scope of such bounds,…
Learning Landmark-Based Ensembles with Random Fourier Features and Gradient Boosting
Léo Gautheron, Pascal Germain, Amaury Habrard +3
We propose a Gradient Boosting algorithm for learning an ensemble of kernel functions adapted to the task at hand. Unlike state-of-the-art Multiple Kernel Learning techniques that…
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…
On Generalizing the C-Bound to the Multiclass and Multi-label Settings
Francois Laviolette, Emilie Morvant, Liva Ralaivola +1
The 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…
Domain adaptation of weighted majority votes via perturbed variation-based self-labeling
Emilie Morvant
In machine learning, the domain adaptation problem arrives when the test (target) and the train (source) data are generated from different distributions. A key applied issue is thu…