5 papers
Hierarchical excitatory processes for modelling event-time data in the presence of exogenous stimuli
Francesco Sanna Passino, Nicholas A. Heard, Jeffrey W. Brown +2
We introduce the Hierarchical Excitatory Process (HEP), a flexible point process model for event-time data observed under repeated external stimuli. The proposed framework models t…
Recovering manifold structure in LLM responses through a joint Euclidean mirror
Maximilian Baum, Aranyak Acharyya, Tianyi Chen +5
Understanding the behavior of black-box large language models and determining effective means of comparing their performance is a key task in modern machine learning. We consider h…
Spectral embedding of inhomogeneous Poisson processes on multiplex networks
Joshua Corneck, Edward A. K. Cohen, Francesco Sanna Passino
In many real-world networks, data on the edges evolve in continuous time, naturally motivating representations based on point processes. Heterogeneity in edge types further gives r…
Statistical hypothesis testing for differences between layers in dynamic multiplex networks
Maximilian Baum, Francesco Sanna Passino, Axel Gandy
With the emergence of dynamic multiplex networks, corresponding to graphs where multiple types of edges evolve over time, a key inferential task is to determine whether the layers…
Simultaneous global and local clustering in multiplex networks with covariate information
Joshua Corneck, Edward A. K. Cohen, James S. Martin +3
Understanding both global and layer-specific group structures is useful for uncovering complex patterns in networks with multiple interaction types. In this work, we introduce a ne…