Title: Tracking Temporal Dynamics of Vector Sets with Gaussian Process
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Spatial Statistics, Gaussian Process, Interpretable Representation Learning

Abstract: Understanding the temporal evolution of sets of vectors is a fundamental challenge across various domains, including ecology, crime analysis, and linguistics. 
For instance, ecosystem structures evolve due to interactions among plants, herbivores, and carnivores; the spatial distribution of crimes shifts in response to societal changes; 
and word embedding vectors reflect cultural and semantic trends over time. 
However, analyzing such time-varying sets of vectors is challenging due to their complicated structures, which also evolve over time. 
In this work, we propose a novel method for modeling the distribution underlying each set of vectors using infinite-dimensional Gaussian processes. 
By approximating the latent function in the Gaussian process with Random Fourier Features, we obtain compact and comparable vector representations over time. 
This enables us to track and visualize temporal transitions of vector sets in a low-dimensional space.
We apply our method to both sociological data (crime distributions) and linguistic data (word embeddings), demonstrating its effectiveness in capturing temporal dynamics. 
Our results show that the proposed approach provides interpretable and robust representations, offering a powerful framework for analyzing structural changes in temporally indexed vector sets across diverse domains.

TL;DR: We propose a method that models vector sets as distributions via Gaussian processes, enabling interpretable low-dimensional tracking of temporal dynamics in data like crime patterns and word embeddings.