A faster sampler for discrete determinantal point processes
S Barthelmé, N Tremblay… - … Conference on Artificial …, 2023 - proceedings.mlr.press
Abstract Discrete Determinantal Point Processes (DPPs) have a wide array of potential
applications for subsampling datasets. They are however held back in some cases by the …
applications for subsampling datasets. They are however held back in some cases by the …
Scalable mcmc sampling for nonsymmetric determinantal point processes
A determinantal point process (DPP) is an elegant model that assigns a probability to every
subset of a collection of $ n $ items. While conventionally a DPP is parameterized by a …
subset of a collection of $ n $ items. While conventionally a DPP is parameterized by a …
On determinantal point processes with nonsymmetric kernels
P Arnaud - arXiv preprint arXiv:2406.03360, 2024 - arxiv.org
Determinantal point processes (DPPs for short) are a class of repulsive point processes.
They have found some statistical applications to model spatial point pattern datasets with …
They have found some statistical applications to model spatial point pattern datasets with …
Training asymmetric kernels of determinantal point processes
K Miyaguchi, T Osogami - US Patent App. 16/864,258, 2021 - Google Patents
Abstract Determinantal Point Process-based predictions are provided by training an
asymmetric kernel of a Determinantal Point Process (DPP) from a training data set by …
asymmetric kernel of a Determinantal Point Process (DPP) from a training data set by …