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Found 6 papers, 0 papers with code
Semisupervised regression in latent structure networks on unknown manifolds
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.
Estimating Higher-Order Mixed Memberships via the $\ell_{2,\infty}$ Tensor Perturbation Bound
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.
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms
Sparse Principal Component Analysis (PCA) is a prevalent tool across a plethora of subfields of applied statistics.
Nonparametric Two-Sample Hypothesis Testing for Random Graphs with Negative and Repeated Eigenvalues
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
On Two Distinct Sources of Nonidentifiability in Latent Position Random Graph Models
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.
Vertex Nomination, Consistent Estimation, and Adversarial Modification
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.
