6 citations · 11 across the 6 of their papers we have counts for
6 papers
Federated Wasserstein Distance
Alain Rakotomamonjy, Kimia Nadjahi, Liva Ralaivola
We introduce a principled way of computing the Wasserstein distance between two distributions in a federated manner. Namely, we show how to estimate the Wasserstein distance betwee…
Personalised Federated Learning On Heterogeneous Feature Spaces
Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz +1
Most personalised federated learning (FL) approaches assume that raw data of all clients are defined in a common subspace i.e. all clients store their data according to the same sc…
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…
Unconfused Ultraconservative Multiclass Algorithms
Ugo Louche, Liva Ralaivola
We tackle the problem of learning linear classifiers from noisy datasets in a multiclass setting. The two-class version of this problem was studied a few years ago by, e.g. Bylande…