Publications

Statistics

Statistiques non locales dans les images : modélisation, estimation et échantillonnage

In this thesis we study two non-localstatistics in images from a probabilistic point of view: spatialredundancy and convolutional neural network features. Moreprecisely, we are interested in the estimation and detection ofspatial redundancy in…

Valentin de Bortoli

Signal and Image processing

The Use of Inertial Measurement Units for the Study of Free Living Environment Activity Assessment: A Literature Review

Sylvain Jung, Mona Michaud, Laurent Oudre, Eric Dorveaux, Louis Gorintin, Nicolas Vayatis, Damien Ricard

Sensors - 2020

Machine Learning

Unsupervised Multi-Source Domain Adaptation for Regression

We consider the problem of unsupervised domain adaptation from multiple sources in a regression setting. We propose in this work an original method to take benefit of different sources using a weighted combination of the sources. For this purpose,…

Guillaume Richard, Antoine de Mathelin, Georges Hébrail, Mathilde Mougeot, Nicolas Vayatis

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - 2020

Neurons and Cognition

An opinion paper on the maintenance of robustness: Towards a multimodal and intergenerational approach using digital twins

The increasing number of frail elderly people in our aging society is becoming problematic: about 11% of community-dwelling older persons are frail and another 42% are pre-frail. Consequently, a major challenge in the coming years will be to test…

Pierre‐paul Vidal, Alienor Vienne‐jumeau, Albane Moreau, Catherine Vidal, Danping Wang, Julien Audiffren, Ioannis Bargiotas, Rémi Barrois, Stéphane Buffat, Clément Dubost, Jean-Michel Ghidaglia, Christophe Labourdette, Juan Mantilla, Laurent Oudre, Flavien Quijoux, Matthieu P Robert, Alain Yelnik, Damien Ricard, Nicolas Vayatis

Aging Medicine - 2020

Combinatorics

Apprentissage efficient dans les problèmes de semi-bandits stochastiques combinatoires

Combinatorial stochastic semi-bandits appear naturally in many contexts where the exploration/exploitation dilemma arises, such as web content optimization (recommendation/online advertising) or shortest path routing methods. This problem is…

Pierre Perrault

Engineering Sciences

ON THE VALIDATION AND USE OF A SIMPLIFIED MODEL OF AEOLIAN VIBRATION OF OVERHEAD LINES FOR PARAMETRIC STUDIES

Emmanuel Cieren, John Redford, Maxime Guéguin, Olivier Allix, Christine Yang, Fikri Hafid, Jean-Michel Ghidaglia

XI International Conference on Structural Dynamics - 2020

Ecosystems

Hough transform implementation to evaluate the morphological variability of the moon jellyfish (Aurelia spp.)

Variations of the animal body plan morphology and morphometry can be used as prognostic tools of their habitat quality. The potential of the moon jellyfish (Aurelia spp.) as a new model organism has been poorly tested. However, as a tetramerous…

Céline Lacaux, Agnès Desolneux, Justine Gadreaud, Bertrand Martin-Garin, Alain Thiéry

General Mathematics

Bandit algorithms for recommender system optimization

In this PhD thesis, we study the optimization of recommender systems with the objective of providing more refined suggestions of items for a user to benefit.The task is modeled using the multi-armed bandit framework.In a first part, we look upon two…

Matthieu Jedor

General Mathematics

Sequential learning and stochastic optimization of convex functions

In this thesis we study several machine learning problems that are all linked with the minimization of a noisy function, which will often be convex.Inspired by real-life applications we focus on sequential learning problems which consist in treating…

Xavier Fontaine

Artificial Intelligence

Transfer Learning on Decision Tree with Class Imbalance

Ludovic Minvielle, Mounir Atiq, Sergio Peignier, Mathilde Mougeot

31st IEEE International Conference on Tools with Artificial Intelligence - 2019