Learning Determinantal Point Processes in Sublinear Time
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.
Cite
Text
Dupuy and Bach. "Learning Determinantal Point Processes in Sublinear Time." International Conference on Artificial Intelligence and Statistics, 2018.Markdown
[Dupuy and Bach. "Learning Determinantal Point Processes in Sublinear Time." International Conference on Artificial Intelligence and Statistics, 2018.](https://mlanthology.org/aistats/2018/dupuy2018aistats-learning/)BibTeX
@inproceedings{dupuy2018aistats-learning,
title = {{Learning Determinantal Point Processes in Sublinear Time}},
author = {Dupuy, Christophe and Bach, Francis R.},
booktitle = {International Conference on Artificial Intelligence and Statistics},
year = {2018},
pages = {244-257},
url = {https://mlanthology.org/aistats/2018/dupuy2018aistats-learning/}
}