Edit-Based Flow Matching for Temporal Point Processes

MCML Authors

Marcel Kollovieh

→ Group Stephan Günnemann
 Data Analytics & Machine Learning

Stephan Günnemann

Prof. Dr.

Core PI

Data Analytics & Machine Learning

Abstract

Temporal point processes (TPPs) are a fundamental tool for modeling event sequences in continuous time, but most existing approaches rely on autoregressive parameterizations that are limited by their sequential sampling. Recent non-autoregressive, diffusion-style models mitigate these issues by jointly interpolating between noise and data through event insertions and deletions in a discrete Markov chain. In this work, we generalize this perspective and introduce an Edit Flow process for TPPs that transports noise to data via insert, delete, and substitute edit operations. By learning the instantaneous edit rates within a continuous-time Markov chain framework, we attain a flexible and efficient model that effectively reduces the total number of necessary edit operations during generation. Empirical results demonstrate the generative flexibility of our unconditionally trained model in a wide range of unconditional and conditional generation tasks on benchmark TPPs.

inproceedings LLK+26

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ICLR 2026

14th International Conference on Learning Representations. Rio de Janeiro, Brazil, Apr 23-27, 2026.

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Authors

D. Lüdke • M. Lienen • M. Kollovieh • S. Günnemann

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Links

URL

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Research Area

 A3 | Computational Models