BINOIS , Mickaël

This paper focuses on multi-label learning from small amounts of labelled data. We demonstratethat the binary-relevance extension of the interpolated label propagation algorithm, the harmonicfunction, is a competitive learning method with respect to many widely-used evaluation measures.This is achieved by a new transition matrix that better...

International audience

Game theory finds nowadays a broad range of applications in engineering and machine learning. However, in a derivative-free, expensive black-box context, very few algorithmic solutions are available to find game equilibria. Here, we propose a novel Gaussian-process based approach for solving games in this context. We follow a...

An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations, recovering the set of optimal compromise solutions generally requires numerous observations and is less interpretable since this set tends to grow larger with the...

We present an innovative computational methodology based on statistical learning multiobjective optimization to optimize highly efficient and robust metasurface designs. We optimize a large-scale 3D metalenses in the visible regime. Besides, we extended our multiobjective optimization to consider the fabrication imperfections for a robust beam...