70 citations · 198 across the 23 of their papers we have counts for
16 papers · 1 filter
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…
Differentially Private Sliced Wasserstein Distance
Alain Rakotomamonjy, Liva Ralaivola
Developing machine learning methods that are privacy preserving is today a central topic of research, with huge practical impacts. Among the numerous ways to address privacy-preser…
Photonic Differential Privacy with Direct Feedback Alignment
Ruben Ohana, Hamlet J. Medina Ruiz, Julien Launay +4
Optical Processing Units (OPUs) -- low-power photonic chips dedicated to large scale random projections -- have been used in previous work to train deep neural networks using Direc…
Partial Trace Regression and Low-Rank Kraus Decomposition
Hachem Kadri, Stéphane Ayache, Riikka Huusari +2
The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs. We here introduce an even more genera…
Quantum Bandits
Balthazar Casalé, Giuseppe Di Molfetta, Hachem Kadri +1
We consider the quantum version of the bandit problem known as {\em best arm identification} (BAI). We first propose a quantum modeling of the BAI problem, which assumes that both…
QuicK-means: Acceleration of K-means by learning a fast transform
Luc Giffon, Valentin Emiya, Liva Ralaivola +1
K-means -- and the celebrated Lloyd algorithm -- is more than the clustering method it was originally designed to be. It has indeed proven pivotal to help increase the speed of man…
Frank-Wolfe Algorithm for the Exact Sparse Problem
Farah Cherfaoui, Valentin Emiya, Liva Ralaivola +1
In this paper, we study the properties of the Frank-Wolfe algorithm to solve the \ExactSparse reconstruction problem. We prove that when the dictionary is quasi-incoherent, at each…
From Cutting Planes Algorithms to Compression Schemes and Active Learning
Liva Ralaivola, Ugo Louche
Cutting-plane methods are well-studied localization(and optimization) algorithms. We show that they provide a natural framework to perform machinelearning ---and not just to solve…
Greedy methods, randomization approaches and multi-arm bandit algorithms for efficient sparsity-constrained optimization
A Rakotomamonjy, S Koço, Liva Ralaivola
Several sparsity-constrained algorithms such as Orthogonal Matching Pursuit or the Frank-Wolfe algorithm with sparsity constraints work by iteratively selecting a novel atom to add…
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 where the propos…
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…
Confusion Matrix Stability Bounds for Multiclass Classification
Pierre Machart, Liva Ralaivola
In this paper, we provide new theoretical results on the generalization properties of learning algorithms for multiclass classification problems. The originality of our work is tha…
Stochastic Low-Rank Kernel Learning for Regression
Pierre Machart, Thomas Peel, Liva Ralaivola +2
We present a novel approach to learn a kernel-based regression function. It is based on the useof conical combinations of data-based parameterized kernels and on a new stochastic c…
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary -Mixing Processes
Liva Ralaivola, Marie Szafranski, Guillaume Stempfel
Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned from independently and identically distributed (IID) data, and it is particularly so for…