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Vincent Schellekens
PhD student

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Pending publications

  1. Asymmetric compressive learning guarantees with applications to quantized sketches
    Vincent Schellekens, Laurent Jacques
    Submitted.
    (More)
  2. Sketching Datasets for Large-Scale Learning
    Rémi Gribonval, Antoine Chatalic, Nicolas Keriven, Vincent Schellekens, Laurent Jacques, Philip Schniter
    Submitted.
    (More)

Journal papers

  1. Breaking the waves: asymmetric random periodic features for low-bitrate kernel machines
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    Vincent Schellekens, Laurent Jacques
    Accepted in: Information and Inference: A Journal of the IMA.
    (More)
  2. Compressive Learning with Privacy Guarantees
    Antoine Chatalic, Vincent Schellekens, Florimond Houssiau, Yves-Alexandre de Montjoye, Laurent Jacques, Rémi Gribonval
    Accepted in: Information and Inference: A Journal of the IMA.
    (More)
  3. Quantized Compressive K-Means
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    Vincent Schellekens, Laurent Jacques
    In: IEEE Signal Processing Letters, Vol. 25, no. 8, p. 1211-1215, 2018.
    (More)

Conference/workshop papers

  1. When compressive learning fails: blame the decoder or the sketch?
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    Vincent Schellekens, Laurent Jacques
    Accepted at the iTWIST'20 workshop.
  2. Compressive Learning of Generative Networks
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    Vincent Schellekens, Laurent Jacques
    Accepted at: ESANN'20, Bruges, Belgium.
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  3. Compressive k-Means with Differential Privacy
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    Vincent Schellekens, Antoine Chatalic, Florimond Houssiau, Yves-Alexandre de Montjoye, Laurent Jacques, Rémi Gribonval
    Accepted at SPARS'19, Toulouse, France.
    (More)
  4. Differentially Private Compressive K-Means
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    Vincent Schellekens, Antoine Chatalic, Florimond Houssiau, Yves-Alexandre de Montjoye, Laurent Jacques, Rémi Gribonval
    Accepted at ICASSP'19, Brighton, UK.
    (More)
  5. Compressive Classification (Machine Learning without learning)
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    Vincent Schellekens, Laurent Jacques
    In: proceedings of the iTWIST'18 workshop, Marseille (France), 2018.
    (More)
  6. Taking the edge off quantization: projected back projection in dithered compressive sensing
    Chunlei Xu, Vincent Schellekens, Laurent Jacques
    In: 2018 IEEE Statistical Signal Processing Workshop (SSP), 2018.
    (More)

Other oral presentations

  1. Generative models as data-driven priors: how to learn them efficiently?
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    Presented at FNRS Contact Group "Wavelets and applications" 2019 meeting (22/11/2019).
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