17 citations
- Centre National de la Recherche ScientifiqueFR3 papers
- Laboratoire d’Informatique Fondamentale de MarseilleFR3 papers
- Laboratoire Hubert CurienFR3 papers
- Center for MathematicaL studies and their ApplicationsFR2 papers
- École Normale Supérieure Paris-SaclayFR2 papers
- Leading Health CareSE2 papers
- Université LavalCA2 papers
- Institut de Recherche en Informatique de ToulouseFR1 paper
- Institut Polytechnique de BordeauxFR1 paper
- Lagrange LaboratoryFR1 paper
- Observatoire de la Côte d’AzurFR1 paper
- Université Toulouse-I-CapitoleFR1 paper
6 papers
Optimal spectral transportation with application to music transcription
Rémi Flamary, Cédric Févotte, Nicolas Courty +1
Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates. In particular, state-of-the-art music transcription…
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…
On the Generalization of the C-Bound to Structured Output Ensemble Methods
François Laviolette, Emilie Morvant, Liva Ralaivola +1
This paper generalizes an important result from the PAC-Bayesian literature for binary classification to the case of ensemble methods for structured outputs. We prove a generic ver…
Stationary Mixing Bandits
Julien Audiffren, Liva Ralaivola
We study the bandit problem where arms are associated with stationary phi-mixing processes and where rewards are therefore dependent: the question that arises from this setting is…
Equivalence of Learning Algorithms
Julien Audiffren, Hachem Kadri
The purpose of this paper is to introduce a concept of equivalence between machine learning algorithms. We define two notions of algorithmic equivalence, namely, weak and strong eq…
PAC-Bayesian Learning and Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette +1
In machine learning, Domain Adaptation (DA) arises when the distribution gen- erating the test (target) data differs from the one generating the learning (source) data. It is well…