no code implementations • 4 May 2023 • Aranyak Acharyya, Joshua Agterberg, Michael W. Trosset, Youngser Park, Carey E. Priebe

We assume that the latent position vectors lie on an unknown one-dimensional curve and are coupled with a response covariate via a regression model.

no code implementations • 16 Dec 2022 • Joshua Agterberg, Anru Zhang

Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it.

no code implementations • 8 Feb 2022 • Joshua Agterberg, Jeremias Sulam

Sparse Principal Component Analysis (PCA) is a prevalent tool across a plethora of subfields of applied statistics.

no code implementations • 17 Dec 2020 • Joshua Agterberg, Minh Tang, Carey Priebe

We propose a nonparametric two-sample test statistic for low-rank, conditionally independent edge random graphs whose edge probability matrices have negative eigenvalues and arbitrarily close eigenvalues.

Graph Embedding Statistics Theory Statistics Theory

no code implementations • 31 Mar 2020 • Joshua Agterberg, Minh Tang, Carey E. Priebe

Two separate and distinct sources of nonidentifiability arise naturally in the context of latent position random graph models, though neither are unique to this setting.

no code implementations • 6 May 2019 • Joshua Agterberg, Youngser Park, Jonathan Larson, Christopher White, Carey E. Priebe, Vince Lyzinski

Given a pair of graphs $G_1$ and $G_2$ and a vertex set of interest in $G_1$, the vertex nomination (VN) problem seeks to find the corresponding vertices of interest in $G_2$ (if they exist) and produce a rank list of the vertices in $G_2$, with the corresponding vertices of interest in $G_2$ concentrating, ideally, at the top of the rank list.

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