Skip to Main content Skip to Navigation
Conference papers

On two ways to use determinantal point processes for Monte Carlo integration

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 interest: 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 multidimen-sional DPP called multivariate Jacobi ensemble. This let us investigate the behavior of both estima-tors on toy problems in yet unexplored regimes.
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download
Contributor : Rémi Bardenet Connect in order to contact the contributor
Submitted on : Wednesday, June 19, 2019 - 3:46:25 PM
Last modification on : Wednesday, April 21, 2021 - 4:18:02 PM


Files produced by the author(s)


  • HAL Id : hal-02160382, version 1


Guillaume Gautier, R. Bardenet, Michal Valko. On two ways to use determinantal point processes for Monte Carlo integration. NEGDEPML 2019 - ICML Workshop on Negative Dependence in ML, Jun 2019, Long Beach, CA, United States. ⟨hal-02160382⟩



Record views


Files downloads