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

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 …

Learning how to predict future events from patterns of past events is difficult when the set of
possible event types is large. Training an unrestricted neural model might overfit to spurious …

We propose a new sparse Granger-causal learning framework for temporal event data. We
focus on a specific class of point processes called the Hawkes process. We begin by …

In recent years, a massive amount of time-stamped sensor data has been generated and
collected by many Internet of Things (IoT) applications, such as advanced automobiles and …

Empirical studies and theoretical models both highlight burstinessas a common temporal
pattern in online behavior. A key driver for burstiness is the self-exciting nature of online …

PoPPy is a Point Process toolbox based on PyTorch, which achieves flexible designing and
efficient learning of point process models. It can be used for interpretable sequential data …

This dissertation discusses the properties of point process models for epidemic diseases
and other clustered phenomena. We present (1) a novel computationally efficient estimator …

Network embeddings into metric spaces provide convenient representations of learned
relationships between features attached to nodes and links. Embeddings into low …

This thesis focuses on modeling event sequences, namely, sequences of discrete events in
continuous time. We build a family of generative probabilistic models that is able to reason …