118 citations · 346 across the 23 of their papers we have counts for
28 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…
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…
Gaussian mixture models as a proxy for interacting language models
Edward L. Wang, Mohammad Sharifi Kiasari, Tianyu Wang +4
Large language models (LLMs) are powerful tools that, in a number of settings, overlap with the results of human pattern recognition and reasoning. Retrieval-augmented generation (…
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…
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…
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…
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…
The Phantom Alignment Strength Conjecture: Practical use of graph matching alignment strength to indicate a meaningful graph match
Donniell E. Fishkind, Felix Parker, Hamilton Sawczuk +5
The alignment strength of a graph matching is a quantity that gives the practitioner a measure of the correlation of the two graphs, and it can also give the practitioner a sense f…
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…
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
Konstantinos Pantazis, Avanti Athreya, Jesús Arroyo +3
Spectral inference on multiple networks is a rapidly-developing subfield of graph statistics. Recent work has demonstrated that joint, or simultaneous, spectral embedding of multip…
On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates
Cong Mu, Angelo Mele, Lingxin Hao +3
In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity.…
Distance-based Positive and Unlabeled Learning for Ranking
Hayden S. Helm, Amitabh Basu, Avanti Athreya +12
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest. The setting we consider…
On a complete and sufficient statistic for the correlated Bernoulli random graph model
Donniell E. Fishkind, Avanti Athreya, Lingyao Meng +2
Inference on vertex-aligned graphs is of wide theoretical and practical importance.There are, however, few flexible and tractable statistical models for correlated graphs, and even…
Inference for multiple heterogeneous networks with a common invariant subspace
Jesús Arroyo, Avanti Athreya, Joshua Cape +3
The development of models for multiple heterogeneous network data is of critical importance both in statistical network theory and across multiple application domains. Although sin…
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Carey E. Priebe, Youngser Park, Joshua T. Vogelstein +6
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering - clustering the vertices of a graph based o…
On estimation and inference in latent structure random graphs
Avanti Athreya, Minh Tang, Youngser Park +1
We define a latent structure model (LSM) random graph as a random dot product graph (RDPG) in which the latent position distribution incorporates both probabilistic and geometric c…
Statistical inference on random dot product graphs: a survey
Avanti Athreya, Donniell E. Fishkind, Keith Levin +7
The random dot product graph (RDPG) is an independent-edge random graph that is analytically tractable and, simultaneously, either encompasses or can successfully approximate a wid…
Semiparametric spectral modeling of the Drosophila connectome
Carey E. Priebe, Youngser Park, Minh Tang +8
We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussia…
A central limit theorem for an omnibus embedding of multiple random graphs and implications for multiscale network inference
Keith Levin, Avanti Athreya, Minh Tang +3
Performing statistical analyses on collections of graphs is of import to many disciplines, but principled, scalable methods for multi-sample graph inference are few. Here we descri…
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…
Community Detection and Classification in Hierarchical Stochastic Blockmodels
Vince Lyzinski, Minh Tang, Avanti Athreya +2
We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs. In our procedure, we first embed a graph into an appropriate Eucli…
A nonparametric two-sample hypothesis testing problem for random dot product graphs
Minh Tang, Avanti Athreya, Daniel L. Sussman +2
We consider the problem of testing whether two finite-dimensional random dot product graphs have generating latent positions that are independently drawn from the same distribution…
A semiparametric two-sample hypothesis testing problem for random dot product graphs
Minh Tang, Avanti Athreya, Daniel L. Sussman +2
Two-sample hypothesis testing for random graphs arises naturally in neuroscience, social networks, and machine learning. In this paper, we consider a semiparametric problem of two-…