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Poster

Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem

Victor-Emmanuel Brunel

Room 517 AB #106

Keywords: [ Learning Theory ] [ Frequentist Statistics ] [ Computational Complexity ]


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.

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