This thesis focuses on graph learning for semi-supervised learning tasks to mitigate the impact of noise in real-world graphs.One approach to learn graphs is using bilevel optimization, whose inner problem optimizes the downstream model, and its outer problem evaluates the performance of the optimized model with respect to a labelling loss and upda...
The purpose of this thesis is to investigate one of the most important challenges related to the development of machine and deep learning methods. Namely, our research is conducted in the setting where models make predictions based on a few labeled examples. Particularly in the context of image classification, the goal of this study is to learn a m...
Le domaine de l'informatique affective, en plein essor depuis quelques décennies, a pour objectif de créer de nouveaux systèmes interactifs capables de percevoir l'état émotionnel de leurs interlocuteurs humains et de s'y adapter automatiquement.Il est de plus en plus fréquent que les datasets ne soient plus disponibles de manière complète et que l...
Les applications d'apprentissage profond se développent rapidement et ne montrent aucun signe de ralentissement. Les topologies des réseaux neuronaux deviennent de plus en plus grandes et complexes pour résoudre les problèmes de la vie réelle.Cette complexité accrue nécessite plus de temps et d'expertise de la part des professionnels, ainsi qu'un i...
Time series is a common data type that has been applied to enormous real-life applications, such as financial analysis, medical diagnosis, environmental monitoring, astronomical discovery, etc. Due to its complex structure, time series raises several challenges in their data processing and mining. The representation of time series plays a key role ...
Learning with partially labeled data, known as semi-supervised learning, deals with problems where few training examples are labeled while available unlabeled data are abundant and valuable for training. In this thesis, we study this framework in the multi-class classification case with a focus on self-learning and feature selection. Self-learning ...
The BigData challenge induces a need for machine learning algorithms to evolve towards large dimensional and more efficient learning engines. Recently, a new direction of research has emerged that consists in analyzing learning methods in the modern regime where the number n and the dimension p of data samples are commensurately large. Compared to ...
