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Guillaume Gautier

2 accepted papers

2019

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

NeurIPS 2019poster

When approximating an integral by a weighted sum of function evaluations, determinantal point processes (DPPs) provide a way to enforce repulsion between the evaluation points. This negative dependence is encoded by a kernel. Fifteen years before the discovery of DPPs, Ermakov & Zolotukhin (EZ, 1960…

2017

Zonotope Hit-and-run for Efficient Sampling from Projection DPPs

ICML 2017poster

Determinantal point processes (DPPs) are distributions over sets of items that model diversity using kernels. Their applications in machine learning include summary extraction and recommendation systems. Yet, the cost of sampling from a DPP is prohibitive in large-scale applications, which has trigg…