10 papers
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…
Vertex misalignment and changepoint localization in network time series
Tianyi Chen, Mohammad Sharifi Kiasari, Sijing Yu +5
Inference for time series of networks often relies on accurate vertex correspondence between network realizations at different times. In practice, however, such vertex alignments c…
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…
KRAFTY: Khatri-Rao Framework for Joint Cluster Recovery
Siyi Gao, Zachary Lubberts, Marianna Pensky
When multiple datasets describe complementary information about the same set of entities, for example, brain scans of an individual over time, global trade network across years, or…
Linking Warm Dark Matter to Merger Tree Histories via Deep Learning Networks
Ilem Leisher, Paul Torrey, Alex M. Garcia +11
Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. Cosmological simulations can represent this series of mergers as a graph-like ``tree''…
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…