Continual Learning for Time-to-Event Modeling

Manisha Dubey, P. K. Srijith, Maunendra Sankar Desarkar

Keywords: Time-to-Event Modeling, Hawkes Process, Continual Learning

TL;DR: We propose HyperHawkes, a hypernetwork based continually learning temporal point process for continuous modeling of time-to-event sequences with minimal forgetting.

Abstract: Temporal point process serves as an essential tool for modeling time-to-event data in continuous time space. Despite having massive amounts of event sequence data from various domains like social media, healthcare etc., real world application of temporal point process are not capable of thriving in continually evolving environment with minimal supervision while retaining previously learnt knowledge. To tackle this, we propose HyperHawkes, a hypernetwork based continually learning temporal point process for continuous modeling of time-to-event sequences with minimal forgetting. We demonstrate the application of the proposed framework through our experiments on two real-world datasets.