Magnet is a joint team between the University of Lille within the CRIStAL and Inria Lille Nord Europe. The Magnet project aims to design new machine learning based methods geared towards mining information networks. Information networks are large collections of interconnected data and documents like citation networks and blog networks among others. For this, we will define new structured prediction methods for (networks of) texts based on machine learning algorithms in graphs. Such algorithms include node classification, link prediction, clustering and probabilistic modeling of graphs. Envisioned applications include browsing, monitoring and recommender systems, and more broadly information extraction in information networks. Application domains cover social networks for cultural data and e-commerce, and biomedical informatics.
Specifically, our main objectives are :
Each item will also be studied in contexts where little (if any) supervision is available. Therefore, semi-supervised and unsupervised learning will be considered throughout the project.
Apprentissage et graphiques décentralisés
Integrated privacy-preserving AI
Optimisation décentralisée et respectueuse de la confidentialité des données pour l'apprentissage statistique
Graph-based Machine Learning for Linguistic Structure Prediction
Tractable probabiistic models for large scale networks
Privacy Preserving Machine Learning
Decentralized Machine Learning under Constraints
Adaptive Graph Learning with Applications to Natural Language Processing
Semi-supervised clustering in graphs 07/12/2017
Task driven representation learning 29/05/2017