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Sampling from a k-DPP without looking at all items
[article]
2020
arXiv
pre-print
Determinantal point processes (DPPs) are a useful probabilistic model for selecting a small diverse subset out of a large collection of items, with applications in summarization, stochastic optimization, active learning and more. Given a kernel function and a subset size k, our goal is to sample k out of n items with probability proportional to the determinant of the kernel matrix induced by the subset (a.k.a. k-DPP). Existing k-DPP sampling algorithms require an expensive preprocessing step
arXiv:2006.16947v1
fatcat:v4nuxiebpfaepd6ououm673k6m