Can I read Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem on EtoBox?

Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem by Brunel, Victor-Emmanuel is a scholarly article available to read on EtoBox.

What is Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem about?

Symmetric determinantal point processes (DPP's) are a class of probabilistic models that encode the random selection of items that exhibit a repulsive behavior. They have attracted a lot of attention in machine learning, when 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.

Author

Brunel, Victor-Emmanuel

Published

2018

Language

EN