Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

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

Learning Granger causality for general point processes is a very challenging task. We
propose an effective method learning Granger causality for a special but significant type of …

Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …

We design a new nonparametric method that allows one to estimate the matrix of integrated
kernels of a multivariate Hawkes process. This matrix not only encodes the mutual …

Causality is crucial to understanding the mechanisms behind complex systems and making
decisions that lead to intended outcomes. Event sequence data is widely collected from …

We study statistical aspects of state-dependent Hawkes processes, which are an extension
of Hawkes processes where a self-and cross-exciting counting process and a state process …

Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …

How to cluster event sequences generated via different point processes is an interesting and
important problem in statistical machine learning. To solve this problem, we propose and …