6 papers · 1 filter
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…
Euclidean mirrors and first-order changepoints in network time series
Tianyi Chen, Zachary Lubberts, Avanti Athreya +2
We describe a model for a network time series whose evolution is governed by an underlying stochastic process, known as the latent position process, in which network evolution can…
Dynamic networks clustering via mirror distance
Runbing Zheng, Avanti Athreya, Marta Zlatic +2
The classification of different patterns of network evolution, for example in brain connectomes or social networks, is a key problem in network inference and modern data science. B…
Procrustes Problems on Random Matrices
Hajg Jasa, Ronny Bergmann, Christian Kümmerle +2
Meaningful comparison between sets of observations often necessitates alignment or registration between them, and the resulting optimization problems range in complexity from those…
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
Guodong Chen, Jesús Arroyo, Avanti Athreya +7
This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anom…
Euclidean mirrors and dynamics in network time series
Avanti Athreya, Zachary Lubberts, Youngser Park +1
Analyzing changes in network evolution is central to statistical network inference, as underscored by recent challenges of predicting and distinguishing pandemic-induced transforma…