Vincent Schellekens — accepted AI-conference papers

2023

Signal Processing with Optical Quadratic Random Sketches

Rémi Delogne, Vincent Schellekens, Laurent Daudet, Laurent Jacques

ICASSP 2023accepted

Random data sketching (or projection) is now a classical technique enabling, for instance, approximate numerical linear algebra and machine learning algorithms with reduced computational complexity and memory. In this context, the possibility of performing data processing (such as pattern detection…

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2019

Differentially Private Compressive K-means

Vincent Schellekens, Antoine Chatalic, Florimond Houssiau, Yves-Alexandre de Montjoye, Laurent Jacques, Rémi Gribonval

ICASSP 2019accepted

This work addresses the problem of learning from large collections of data with privacy guarantees. The sketched learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, from which the learning task is then performed…

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