Learning Determinantal Point Processes in Sublinear Time

Christophe Dupuy 1, 2 Francis Bach 2, 3
2 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : We propose a new class of determinantal point processes (DPPs) which can be manipulated for inference and parameter learning in potentially sublinear time in the number of items. This class, based on a specific low-rank factorization of the marginal kernel, is particularly suited to a subclass of continuous DPPs and DPPs defined on exponentially many items. We apply this new class to modelling text documents as sampling a DPP of sentences, and propose a conditional maximum likelihood formulation to model topic proportions, which is made possible with no approximation for our class of DPPs. We present an application to document summarization with a DPP on $2^{500}$ items.
Type de document :
Pré-publication, Document de travail
Under review for AISTATS 2017. 2016
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Contributeur : Christophe Dupuy <>
Soumis le : mercredi 19 octobre 2016 - 10:55:26
Dernière modification le : mercredi 30 janvier 2019 - 11:07:46


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  • HAL Id : hal-01383742, version 1
  • ARXIV : 1610.05925



Christophe Dupuy, Francis Bach. Learning Determinantal Point Processes in Sublinear Time. Under review for AISTATS 2017. 2016. 〈hal-01383742〉



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