Skip to yearly menu bar Skip to main content


Contributed talk
in
Workshop: Negative Dependence: Theory and Applications in Machine Learning

On Two Ways to use Determinantal Point Processes for Monte Carlo Integration

Guillaume Gautier

[ ]
[ Video
2019 Contributed talk

Abstract:

This paper focuses on Monte Carlo integration with determinantal point processes (DPPs) which enforce negative dependence between quadrature nodes. We survey the properties of two unbiased Monte Carlo estimators of the integral of inter- est: a direct one proposed by Bardenet & Hardy (2016) and a less obvious 60-year-old estimator by Ermakov & Zolotukhin (1960) that actually also relies on DPPs. We provide an efficient implementation to sample exactly a particular multidimensional DPP called multivariate Jacobi ensemble. This let us investigate the behavior of both estimators on toy problems in yet unexplored regimes.

Chat is not available.