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Spectral clustering and the high-dimensional stochastic blockmodel
Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social ne tworks, representing people who communicate with each other, areExpand
Matrix estimation by Universal Singular Value Thresholding
Consider the problem of estimating the entries of a large matrix, when the observed entries are noisy versions of a small random fraction of the original entries. This problem has received widespreadExpand
Estimating and understanding exponential random graph models
We introduce a method for the theoretical analysis of exponential random graph models. The method is based on a large-deviations approximation to the normalizing constant shown to be consistent usingExpand
A NEW METHOD OF NORMAL APPROXIMATION
We introduce a new version of Stein's method that reduces a large class of normal approximation problems to variance bounding exercises, thus making a connection between central limit theorems andExpand
Random graphs with a given degree sequence
Large graphs are sometimes studied through their degree sequences (power law or regular graphs). We study graphs that are uniformly chosen with a given degree sequence. Under mild conditions, it isExpand
Fluctuations of eigenvalues and second order Poincaré inequalities
Linear statistics of eigenvalues in many familiar classes of random matrices are known to obey gaussian central limit theorems. The proofs of such results are usually rather difficult, involving hardExpand
A new perspective on least squares under convex constraint
Consider the problem of estimating the mean of a Gaussian random vector when the mean vector is assumed to be in a given convex set. The most natural solution is to take the Euclidean projection ofExpand
The large deviation principle for the Erdős-Rényi random graph
TLDR
The formulation and proof of the main result uses the recent development of the theory of graph limits by Lovasz and coauthors and Szemeredi's regularity lemma from graph theory to establish a large deviation principle under an appropriate topology. Expand
Nonlinear large deviations
We present a general technique for computing large deviations of nonlinear functions of independent Bernoulli random variables. The method is applied to compute the large deviation rate functions forExpand
A generalization of the Lindeberg principle
We generalize Lindeberg’s proof of the central limit theorem to an invariance principle for arbitrary smooth functions of independent and weakly dependent random variables. The result is applied toExpand
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