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Vincent Schellekens
Vincent Schellekens
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Title
Cited by
Cited by
Year
Differentially private compressive k-means
V Schellekens, A Chatalic, F Houssiau, YA De Montjoye, L Jacques, ...
ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019
232019
Quantized Compressive K-Means
V Schellekens, L Jacques
IEEE Signal Processing Letters 25 (8), 1211-1215, 2018
232018
Compressive learning with privacy guarantees
A Chatalic, V Schellekens, F Houssiau, YA De Montjoye, L Jacques, ...
Information and Inference: A Journal of the IMA 11 (1), 251-305, 2022
112022
Sketching data sets for large-scale learning: Keeping only what you need
R Gribonval, A Chatalic, N Keriven, V Schellekens, L Jacques, P Schniter
IEEE Signal Processing Magazine 38 (5), 12-36, 2021
92021
Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines
V Schellekens, L Jacques
Information and Inference: A Journal of the IMA, 2020
72020
Sketching datasets for large-scale learning (long version)
R Gribonval, A Chatalic, N Keriven, V Schellekens, L Jacques, P Schniter
arXiv preprint arXiv:2008.01839, 2020
62020
Compressive classification (machine learning without learning)
V Schellekens, L Jacques
arXiv preprint arXiv:1812.01410, 2018
42018
Compressive k-means with differential privacy
V Schellekens, A Chatalic, F Houssiau, YA de Montjoye, L Jacques, ...
SPARS 2019-Signal Processing with Adaptive Sparse Structured Representations …, 2019
32019
Extending the Compressive Statistical Learning Framework: Quantization, Privacy, and Beyond
V Schellekens
UCLouvain, Belgium, 2021
22021
Compressive Learning of Generative Networks
V Schellekens, L Jacques
28th European Symposium on Artificial Neural Networks, Computational …, 2020
22020
When compressive learning fails: blame the decoder or the sketch?
V Schellekens, L Jacques
arXiv preprint arXiv:2009.08273, 2020
22020
Compressive clustering of high-dimensional datasets by 1-bit sketching
V Schellekens, L Jacques
PhD thesis, 2017
22017
Asymmetric compressive learning guarantees with applications to quantized sketches
V Schellekens, L Jacques
IEEE Transactions on Signal Processing 70, 1348 - 1360, 2022
12022
Taking the edge off quantization: projected back projection in dithered compressive sensing
C Xu, V Schellekens, L Jacques
2018 IEEE Statistical Signal Processing Workshop (SSP), 203-207, 2018
12018
ROP inception: signal estimation with quadratic random sketching
R Delogne, V Schellekens, L Jacques
arXiv preprint arXiv:2205.08225, 2022
2022
PYCLE: a Python Compressive Learning toolbox
V Schellekens
https://github.com/schellekensv/pycle, 2020
2020
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