Magnet is a research team at INRIA Lille Nord Europe and in CRIStAL (CNRS UMR 9189, University of Lille)
Important!
We have open positions (Tenured, PhD., postdocs, engineers, internships) on the following topics:- Decentralized Machine Learning and Privacy
- Statistical Learning with Privacy Constraints and Fairness
- Machine Learning and Natural Language Processing
Team presentation
A primary objective of Magnet is in making artificial intelligence more acceptable to society by solving some ethical issues of Machine Learning (ML) and on empowering end users of artificial intelligence. From a scientific perspective we focus on privacy, fairness, (data) sobriety. Our approaches are typically based on the common theme of leveraging the relationships between data and between learning objectives. We study graph-based machine learning methods which are the common foundations of the research group and we rely on methods coming from statistical and computational learning theory, graph theory, representation learning, (distributed) optimization and statistics.
We are mainly interested in provable properties for machine learning algorithms but we also consider more empirical work. Our application domains cover health, mobility, social sciences, voice technologies.
Research themes
Our research is organized along three main axes:
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Mining and learning in graphs: we study the trade-off between predictive accuracy, computational complexity and verification of ethical properties for graph-based learning algorithms.
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Machine Learning for Natural Language Processing: we study how to enhance NLP methods with graph-based learning algorithms and how to develop ethical learning algorithms in NLP. The objective is to go beyond vectorial classification to solve task like coreference resolution and entity linking, temporal structure prediction, and discourse parsing.
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Decentralized Machine Learning: we address the problem of learning in a private, fair and energy efficient way when users and data are naturally distributed in a network. From an algorithmic perspective, we study federated learning and fully decentralized learning and optimization. We also consider research on a global and holistic level, in complex pipelines that involve learning.
International and industrial relations
- UCLA, UCL, Alan Turing Institute, MPI
- Lille Hospital, Université de Lille
