Learning Determinantal Point Processes in Sublinear Time

@InProceedings{pmlr-v84-dupuy18a,
  title = 	 {Learning Determinantal Point Processes in Sublinear Time},
  author = 	 {Dupuy, Christophe and Bach, Francis},
  booktitle = 	 {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
  pages = 	 {244--257},
  year = 	 {2018},
  editor = 	 {Storkey, Amos and Perez-Cruz, Fernando},
  volume = 	 {84},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--11 Apr},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v84/dupuy18a/dupuy18a.pdf},
  url = 	 {https://proceedings.mlr.press/v84/dupuy18a.html},
  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 to the power 500 items.}
}