1 citations · 1 across the 4 of their papers we have counts for
8 papers
MM: A general method to perform various data analysis tasks from a differentially private sketch
Florimond Houssiau, Vincent Schellekens, Antoine Chatalic +2
Differential privacy is the standard privacy definition for performing analyses over sensitive data. Yet, its privacy budget bounds the number of tasks an analyst can perform with…
ROP inception: signal estimation with quadratic random sketching
Rémi Delogne, Vincent Schellekens, Laurent Jacques
Rank-one projections (ROP) of matrices and quadratic random sketching of signals support several data processing and machine learning methods, as well as recent imaging application…
When compressive learning fails: blame the decoder or the sketch?
Vincent Schellekens, Laurent Jacques
In compressive learning, a mixture model (a set of centroids or a Gaussian mixture) is learned from a sketch vector, that serves as a highly compressed representation of the datase…
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines
Vincent Schellekens, Laurent Jacques
Many signal processing and machine learning applications are built from evaluating a kernel on pairs of signals, e.g. to assess the similarity of an incoming query to a database of…
Compressive Learning of Generative Networks
Vincent Schellekens, Laurent Jacques
Generative networks implicitly approximate complex densities from their sampling with impressive accuracy. However, because of the enormous scale of modern datasets, this training…
Compressive Classification (Machine Learning without learning)
Vincent Schellekens, Laurent Jacques
Compressive learning is a framework where (so far unsupervised) learning tasks use not the entire dataset but a compressed summary (sketch) of it. We propose a compressive learning…