Vincent Schellekens

According to our database, Vincent Schellekens authored at least 14 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Signal Processing with Optical Quadratic Random Sketches.

Rémi Delogne, Vincent Schellekens, Laurent Daudet, Laurent Jacques

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

Asymmetric Compressive Learning Guarantees With Applications to Quantized Sketches.

Vincent Schellekens, Laurent Jacques

IEEE Trans. Signal Process., 2022

M²M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch.

Florimond Houssiau, Vincent Schellekens, Antoine Chatalic, Shreyas Kumar Annamraju, Yves-Alexandre de Montjoye

Proceedings of the Security and Trust Management - 18th International Workshop, 2022

ROP inception: signal estimation with quadratic random sketching.

Rémi Delogne, Vincent Schellekens, Laurent Jacques

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

2021

Extending the compressive statistical learning framework: quantization, privacy and beyond.

Vincent Schellekens

PhD thesis, 2021

Sketching Data Sets for Large-Scale Learning: Keeping only what you need.

Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter

IEEE Signal Process. Mag., 2021

2020

When compressive learning fails: blame the decoder or the sketch?

Vincent Schellekens, Laurent Jacques

CoRR, 2020

Sketching Datasets for Large-Scale Learning (long version).

Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter

CoRR, 2020

Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines.

Vincent Schellekens, Laurent Jacques

CoRR, 2020

Compressive Learning of Generative Networks.

Vincent Schellekens, Laurent Jacques

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019

Differentially Private Compressive K-means.

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

Proceedings of the IEEE International Conference on Acoustics, 2019

2018

Quantized Compressive K-Means.

Vincent Schellekens, Laurent Jacques

IEEE Signal Process. Lett., 2018

Compressive Classification (Machine Learning without learning).

Vincent Schellekens, Laurent Jacques

CoRR, 2018

Taking the Edge off Quantization: Projected Back Projection in Dithered Compressive Sensing.

Chunlei Xu, Vincent Schellekens, Laurent Jacques

Proceedings of the 2018 IEEE Statistical Signal Processing Workshop, 2018