Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes. (arXiv:2207.00486v1 [cs.LG])

A determinantal point process (DPP) is an elegant model that assigns a
probability to every subset of a collection of $n$ items. While conventionally
a DPP is parameterized by a symmetric kernel matrix, removing this symmetry
constraint, resulting in nonsymmetric DPPs (NDPPs), leads to significant
improvements in modeling power and predictive performance. Recent work has
studied an approximate Markov chain Monte Carlo (MCMC) sampling algorithm for
NDPPs restricted to size-$k$ subsets (called $k$-NDPPs). However, the runtime
of this approach is …

arxiv lg mcmc processes sampling scalable