NeurIPS 2025

Addressing Mark Imbalance in Integration-free
Neural Marked Temporal Point Processes
Sishun Liu
RMIT University
Melbourne, Victoria 3000
sishun.liu@student.rmit.edu.au
Ke Deng
RMIT University
Melbourne, Victoria 3000
ke.deng@rmit.edu.au
Yongli Ren
RMIT University
Melbourne, Victoria 3000
yongli.ren@rmit.edu.au
Yan Wang
Macquarie University
Syndey, New South Wales 2000
yan.wang@mq.edu.au
Xiuzhen Zhang
RMIT University
Melbourne, Victoria 3000
xiuzhen.zhang@rmit.edu.au
Abstract
Marked Temporal Point Process (MTPP) has been well studied to model the
event distribution in marked event streams, which can be used to predict the mark
and arrival time of the next event. However, existing studies overlook that the
distribution of event marks is highly imbalanced in many real-world applications,
with some marks being frequent but others rare. The imbalance poses a significant
challenge to the performance of the next event prediction, especially for events of
rare marks. To address this issue, we propose a thresholding method, which learns
thresholds to tune the mark probability normalized by the mark’s prior probability
to optimize mark prediction, rather than predicting the mark directly based on
the mark probability as in existing studies. In conjunction with this method, we
predict the mark first and then the time. In particular, we develop a novel neural
MTPP model to support effective time sampling and estimation of mark probability
without computationally expensive numerical improper integration. Extensive
experiments on real-world datasets demonstrate the superior performance of our
solution against various baselines for the next event mark and time prediction. The
code is available at https://github.com/undes1red/IFNMTPP.
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Introduction
Marked Temporal Point Process (MTPP) models event sequences observed from natural phenomena
(e.g. earthquakes) or generated in human activities (e.g. retweets), where each event has a mark and
an arrival time. MTPP has attracted the att...