21 citations · 30 across the 15 of their papers we have counts for
17 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, such vertex alignments can be mis…
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…
Limiting distributions of ratios of Binomial random variables
Adriel Barretto, Zachary Lubberts
We consider the limiting distribution of the quantity , where and are two independent Binomial random variables with a common success probability and a number…
Perfect Clustering in Nonuniform Hypergraphs
Ga-Ming Angus Chan, Zachary Lubberts
While there has been tremendous activity in the area of statistical network inference on graphs, hypergraphs have not enjoyed the same attention, on account of their relative compl…
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…
Curvature-based Clustering on Graphs
Yu Tian, Zachary Lubberts, Melanie Weber
Unsupervised node clustering (or community detection) is a classical graph learning task. In this paper, we study algorithms, which exploit the geometry of the graph to identify de…
Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels
Joshua Agterberg, Zachary Lubberts, Jesús Arroyo
Modern network datasets are often composed of multiple layers, either as different views, time-varying observations, or independent sample units, resulting in collections of networ…
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…
Entrywise Estimation of Singular Vectors of Low-Rank Matrices with Heteroskedasticity and Dependence
Joshua Agterberg, Zachary Lubberts, Carey Priebe
We propose an estimator for the singular vectors of high-dimensional low-rank matrices corrupted by additive subgaussian noise, where the noise matrix is allowed to have dependence…
Random line graphs and edge-attributed network inference
Zachary Lubberts, Avanti Athreya, Youngser Park +1
We extend the latent position random graph model to the line graph of a random graph, which is formed by creating a vertex for each edge in the original random graph, and connectin…
Multivariate Tight Wavelet Frames with Few Generators and High Vanishing Moments
Youngmi Hur, Zachary Lubberts, Kasso A. Okoudjou
Tight wavelet frames are computationally and theoretically attractive, but most existing multivariate constructions have various drawbacks, including low vanishing moments for the…
Concise Complexity Analyses for Trust-Region Methods
Frank E. Curtis, Zachary Lubberts, Daniel P. Robinson
Concise complexity analyses are presented for simple trust region algorithms for solving unconstrained optimization problems. In contrast to a traditional trust region algorithm, t…
Numerical tolerance for spectral decompositions of random matrices
Avanti Athreya, Michael Kane, Bryan Lewis +5
We precisely quantify the impact of statistical error in the quality of a numerical approximation to a random matrix eigendecomposition, and under mild conditions, we use this to i…
Generalizations of the Maillet Determinant
Youngmi Hur, Zachary Lubberts
We consider several extensions of the Maillet determinant studied by Malo, Turnbull, and Carlitz and Olson, and derive properties of the underlying matrices. In particular, we comp…