Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions
arXiv:2610.12829v1 Announce Type: new Abstract: Determinantal point processes (DPPs) on a finite set are probability models that exhibit repulsion where inter-item similarities are encoded by a positive definite kernel matrix. Scaling DPP learning to large datasets typically requires imposing struc…
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- 2026-10-12 04:00 · arXiv stat.ML
Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions