April 18, 2024, 4:43 a.m. | Hideitsu Hino, Keisuke Yano

stat.ML updates on arXiv.org arxiv.org

arXiv:2404.11024v1 Announce Type: cross
Abstract: This paper investigates the information geometrical structure of a determinantal point process (DPP). It demonstrates that a DPP is embedded in the exponential family of log-linear models. The extent of deviation from an exponential family is analyzed using the $\mathrm{e}$-embedding curvature tensor, which identifies partially flat parameters of a DPP. On the basis of this embedding structure, the duality related to a marginal kernel and an $L$-ensemble kernel is discovered.

abstract arxiv cs.it deviation embedded embedding family information linear math.it math.st paper process stat.ml stat.th tensor the information type

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