RUDI Alessandro

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Affiliations
  • 2017 - 2021
    Apprentissage statistique et parcimonie
  • 2019 - 2021
    Centre de recherche Inria de Paris
  • 2017 - 2021
    Communauté d'universités et établissements Université de Recherche Paris Sciences et Lettres
  • 2017 - 2020
    Département d'Informatique de l'Ecole Normale Supérieure
  • 2021
  • 2020
  • 2019
  • 2018
  • Sampling from Arbitrary Functions via PSD Models.

    Ulysse MARTEAU FEREY, Alessandro RUDI, Francis BACH
    2021
    In many areas of applied statistics and machine learning, generating an arbitrary number of independent and identically distributed (i.i.d.) samples from a given distribution is a key task. When the distribution is known only through evaluations of the density, current methods either scale badly with the dimension or require very involved implementations. Instead, we take a two-step approach by first modeling the probability distribution and then sampling from that model. We use the recently introduced class of positive semi-definite (PSD) models, which have been shown to be efficient for approximating probability densities. We show that these models can approximate a large class of densities concisely using few evaluations, and present a simple algorithm to effectively sample from these models. We also present preliminary empirical results to illustrate our assertions.
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