Temporal point processes (TPP) are probabilistic generative models for continuous-time
event sequences. Neural TPPs combine the fundamental ideas from point process literature …

Large language models have shown astonishing performance on a wide range of reasoning
tasks. In this paper, we investigate whether they could reason about real-world events and …

In this paper, we tackle the important yet under-investigated problem of making long-horizon
prediction of event sequences. Existing state-of-the-art models do not perform well at this …

The neural Hawkes process (Mei & Eisner, 2017) is a generative model of irregularly spaced
sequences of discrete events. To handle complex domains with many event types, Mei et …

Continuous-time event sequences play a vital role in real-world domains such as
healthcare, finance, online shopping, social networks, and so on. To model such data …

As machine learning methods are increasingly used in interactive systems it becomes
common for user experiences to be the result of an ecosystem of machine learning models …

Event sequence, where each event is associated with a marker and a timestamp, is
increasingly ubiquitous in various applications. Accordingly, event forecasting emerges to …

Sequences of labeled events observed at irregular intervals in continuous time are
ubiquitous across various fields. Temporal Point Processes (TPPs) provide a mathematical …

Streams of irregularly occurring events are commonly modeled as a marked temporal point
process. Many real-world datasets such as e-commerce transactions and electronic health …

Cyber-physical systems (CPSs), eg, elevators and autonomous driving systems, are
progressively permeating our everyday lives. To ensure their safety, various analyses need …