Speculative Sampling for Parametric Temporal Point Processes

• URL: http://arxiv.org/abs/2510.20031v1
• Date: Wed, 22 Oct 2025 21:20:26 GMT
• Title: Speculative Sampling for Parametric Temporal Point Processes
• Authors: Marin Biloš, Anderson Schneider, Yuriy Nevmyvaka,
• Abstract summary: temporal point processes are powerful generative models for event sequences.<n>They are commonly specified using autoregressive models that learn the distribution of the next event from the previous events.<n>We propose a novel algorithm based on rejection sampling that enables exact sampling of multiple future values from existing TPP models.
• Score: 9.15731236208975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal point processes are powerful generative models for event sequences that capture complex dependencies in time-series data. They are commonly specified using autoregressive models that learn the distribution of the next event from the previous events. This makes sampling inherently sequential, limiting efficiency. In this paper, we propose a novel algorithm based on rejection sampling that enables exact sampling of multiple future values from existing TPP models, in parallel, and without requiring any architectural changes or retraining. Besides theoretical guarantees, our method demonstrates empirical speedups on real-world datasets, bridging the gap between expressive modeling and efficient parallel generation for large-scale TPP applications.

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