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Takahiro Kawashima

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#machine learning Preprint Oct 2026

Generalized Determinantal Point Processes for Attraction and Repulsion with Distinct Interaction Kernels

The determinant-ratio point process (DRPP), which models repulsion and attraction via a ratio of the determinants of two symmetric positive-definite kernels, is proposed, which preserves the feature-based interpretation of standard DPPs while allowing the two effects to be specified separately.

Hideaki Ishibashi, Takahiro Kawashima, H. Hino · 0 citations

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