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Pré-Publication, Document De Travail Année : 2016

Learning Determinantal Point Processes in Sublinear Time

Résumé

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.
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Dates et versions

hal-01383742 , version 1 (19-10-2016)

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Christophe Dupuy, Francis Bach. Learning Determinantal Point Processes in Sublinear Time. 2016. ⟨hal-01383742⟩
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