Preventing Conflicting Gradients in Neural Marked Temporal Point Processes
Bosser, Tanguy Ben Taieb, Souhaib
Bosser, Tanguy
Ben Taieb, Souhaib
Author
Supervisor
Department
Statistics and Data Science
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Neural Marked Temporal Point Processes (MTPP) are flexible models to capture complex temporal inter-dependencies between labeled events. These models inherently learn two predictive distributions: one for the arrival times of events and another for the types of events, also known as marks. In this study, we demonstrate that learning a MTPP model can be framed as a two-task learning problem, where both tasks share a common set of trainable parameters that are optimized jointly. We show that this often leads to the emergence of conflicting gradients during training, where task-specific gradients are pointing in opposite directions. When such conflicts arise, following the average gradient can be detrimental to the learning of each individual tasks, resulting in overall degraded performance. To overcome this issue, we introduce novel parametrizations for neural MTPP models that allow for separate modeling and training of each task, effectively avoiding the problem of conflicting gradients. Through experiments on multiple real-world event sequence datasets, we demonstrate the benefits of our framework compared to the original model formulations.
Citation
T. Bosser and S. Ben Taieb, “Preventing Conflicting Gradients in Neural Marked Temporal Point Processes”, Accessed: Apr. 03, 2025. [Online]. Available: https://github.com/tanguybosser/grapTPP_tmlr
Source
Transactions on Machine Learning Research
Conference
Keywords
Neural Marked Temporal Point Processes (MTPP), Conflicting gradients, Two-task learning, Gradient conflict resolution, Predictive performance
Subjects
Source
Publisher
Transactions on Machine Learning Research