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

. Catholic University of Louvain, Louvain-la-Neuve, Belgium, (2021)base-search.net (ftunivlouvain:oai:dial.uclouvain.be:boreal:250190).

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Dries Schellekens

Hans-Willi Schellekens

Vincent Tejedor

Vincent Kuhn

Vincent Saderne

 

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Asymmetric Compressive Learning Guarantees With Applications to Quantized Sketches., и . IEEE Trans. Signal Process., (2022)Signal Processing with Optical Quadratic Random Sketches., , , и . ICASSP, стр. 1-5. IEEE, (2023)Sketching Datasets for Large-Scale Learning (long version)., , , , , и . CoRR, (2020)M2M: A General Method to Perform Various Data Analysis Tasks from a Differentially Private Sketch., , , , и . STM, том 13867 из Lecture Notes in Computer Science, стр. 117-135. Springer, (2022)Taking the Edge off Quantization: Projected Back Projection in Dithered Compressive Sensing., , и . SSP, стр. 203-207. IEEE, (2018)Differentially Private Compressive K-means., , , , , и . ICASSP, стр. 7933-7937. IEEE, (2019)When compressive learning fails: blame the decoder or the sketch?, и . CoRR, (2020)Asymmetric compressive learning guarantees with applications to quantized sketches., и . CoRR, (2021)Sketching Data Sets for Large-Scale Learning: Keeping only what you need., , , , , и . IEEE Signal Process. Mag., 38 (5): 12-36 (2021)ROP inception: signal estimation with quadratic random sketching., , и . ESANN, (2022)