Learning Signed Determinantal Point Processes Through the Principal Minor Assignment Problem
Abstract
Symmetric determinantal point processes (DPP) are a class of probabilistic models that encode the random selection of items that have a repulsive behavior. They have attracted a lot of attention in machine learning, where returning diverse sets of items is sought for. Sampling and learning these symmetric DPP's is pretty well understood. In this work, we consider a new class of DPP's, which we call signed DPP's, where we break the symmetry and allow attractive behaviors. We set the ground for learning signed DPP's through a method of moments, by solving the so called principal assignment problem for a class of matrices $K$ that satisfy $K_{i,j}=\pm K_{j,i}$, $i\neq j$, in polynomial time.
Cite
Text
Brunel. "Learning Signed Determinantal Point Processes Through the Principal Minor Assignment Problem." Neural Information Processing Systems, 2018.Markdown
[Brunel. "Learning Signed Determinantal Point Processes Through the Principal Minor Assignment Problem." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/brunel2018neurips-learning/)BibTeX
@inproceedings{brunel2018neurips-learning,
title = {{Learning Signed Determinantal Point Processes Through the Principal Minor Assignment Problem}},
author = {Brunel, Victor-Emmanuel},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {7365-7374},
url = {https://mlanthology.org/neurips/2018/brunel2018neurips-learning/}
}