| 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 | 27 | 2019 |
| Quantized Compressive K-Means V Schellekens, L Jacques IEEE Signal Processing Letters 25 (8), 1211-1215, 2018 | 26 | 2018 |
| 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 | 21 | 2021 |
| 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 | 17 | 2022 |
| 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 | 13 | 2020 |
| 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 | 8 | 2020 |
| Compressive classification (machine learning without learning) V Schellekens, L Jacques arXiv preprint arXiv:1812.01410, 2018 | 7 | 2018 |
| Compressive Learning of Generative Networks V Schellekens, L Jacques 28th European Symposium on Artificial Neural Networks, Computational …, 2020 | 5 | 2020 |
| 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 | 5 | 2019 |
| ROP inception: signal estimation with quadratic random sketching R Delogne, V Schellekens, L Jacques 30th European Symposium on Artificial Neural Networks, Computational …, 2022 | 4 | 2022 |
| When compressive learning fails: blame the decoder or the sketch? V Schellekens, L Jacques arXiv preprint arXiv:2009.08273, 2020 | 2 | 2020 |
| PYCLE: a Python Compressive Learning toolbox V Schellekens https://github.com/schellekensv/pycle, 2020 | 2 | 2020 |
| Compressive clustering of high-dimensional datasets by 1-bit sketching V Schellekens, L Jacques master thesis, Louvain School of Engineering (EPL), UCLouvain, 2017 | 2 | 2017 |
| Cradle-to-gate Life Cycle Assessment of CMOS Logic Technologies L Boakes, MG Bardon, V Schellekens, IY Liu, B Vanhouche, G Mirabelli, ... IEEE International Electron Devices Meeting 2023, 2023 | 1 | 2023 |
| Signal processing with optical quadratic random sketches R Delogne, V Schellekens, L Daudet, L Jacques ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 1 | 2023 |
| Asymmetric compressive learning guarantees with applications to quantized sketches V Schellekens, L Jacques IEEE Transactions on Signal Processing 70, 1348 - 1360, 2022 | 1 | 2022 |
| Extending the Compressive Statistical Learning Framework: Quantization, Privacy, and Beyond V Schellekens UCLouvain, Belgium, 2021 | 1 | 2021 |
| 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 | 1 | 2018 |
| Signal processing after quadratic random sketching with optical units R Delogne, V Schellekens, L Daudet, L Jacques International Symposium on Computational Sensing (ISCS23), 2023 | | 2023 |
| M M: A general method to perform various data analysis tasks from a differentially private sketch F Houssiau, V Schellekens, A Chatalic, SK Annamraju, YA de Montjoye Security and Trust Management (STM) 2022, 2022 | | 2022 |
Laurent JacquesProfessor, ISPGroup, INMA/ICTEAM, UCLouvain, BelgiumVerified email at uclouvain.be
