164 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University College LondonGB15 papers
- Institut national de recherche en sciences et technologies du numériqueFR13 papers
- Modélisation aléatoire de Paris NanterreFR13 papers
- University of LondonGB8 papers
- Centre Inria de l'Université de LilleFR7 papers
- Centre National de la Recherche ScientifiqueFR5 papers
- Laboratoire de Probabilités et Modèles AléatoiresFR5 papers
- Université Paris NanterreFR5 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR3 papers
- Institut de Mathématiques de MarseilleFR3 papers
- Université de LilleFR3 papers
- University of OxfordGB3 papers
11 papers · 1 filter
Model-based Clustering with Missing Not At Random Data
Aude Sportisse, Matthieu Marbac, Fabien Laporte +4
Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at…
Ranking and synchronization from pairwise measurements via SVD
Alexandre d'Aspremont, Mihai Cucuringu, Hemant Tyagi
Given a measurement graph and an unknown signal , we investigate algorithms for recovering from pairwise measurements of the form ; $\…
Non-linear aggregation of filters to improve image denoising
Benjamin Guedj, Juliette Rengot
We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity ba…
A Primer on PAC-Bayesian Learning
Benjamin Guedj
Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility. The present paper aims at providi…
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
Gaël Letarte, Emilie Morvant, Pascal Germain
We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF) method through the lens of the PAC-Bayesian theory. While the primary goal of RFF is to approximate a kern…
Sequential Learning of Principal Curves: Summarizing Data Streams on the Fly
Benjamin Guedj, Le Li
When confronted with massive data streams, summarizing data with dimension reduction methods such as PCA raises theoretical and algorithmic pitfalls. Principal curves act as a nonl…
Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles
John Klein, Mahmoud Albardan, Benjamin Guedj +1
We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models.…
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
Mihai Cucuringu, Hemant Tyagi
Consider an unknown smooth function , and say we are given noisy mod 1 samples of , i.e., , for $x_i \in [0,1]…
PAC-Bayes and Domain Adaptation
Pascal Germain, Amaury Habrard, François Laviolette +1
We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a dif…
Simpler PAC-Bayesian Bounds for Hostile Data
Pierre Alquier, Benjamin Guedj
PAC-Bayesian learning bounds are of the utmost interest to the learning community. Their role is to connect the generalization ability of an aggregation distribution to its emp…
PAC-Bayesian High Dimensional Bipartite Ranking
Benjamin Guedj, Sylvain Robbiano
This paper is devoted to the bipartite ranking problem, a classical statistical learning task, in a high dimensional setting. We propose a scoring and ranking strategy based on the…