Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

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 …

The log-likelihood of a generative model often involves both positive and negative terms. For
a temporal multivariate point process, the negative term sums over all the possible event …

Various incremental evaluation strategies for Datalog have been developed that reuse
computations for small input changes. These methods assume that incrementalization is …

Hawkes process provides an effective statistical framework for analyzing the interactions of
neural spiking activities. Although utilized in many real applications, the classic Hawkes …

This paper provides a reference description, in the form of a deduction system, of Earley's
(1970) context-free parsing algorithm with various speed-ups. Our presentation includes a …

Data-driven decision making is becoming an integral part of manufacturing companies. Data
is collected and commonly used to improve efficiency and produce high quality items for the …