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Consistency of spectral clustering in stochastic block models
We analyze the performance of spectral clustering for community extraction in stochastic block models. We show that, under mild conditions, spectral clustering applied to the adjacency matrix of theExpand
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Distribution-Free Predictive Inference for Regression
TLDR
We develop a general framework for distribution-free predictive inference in regression, using conformal inference. Expand
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Differential privacy for functions and functional data
TLDR
We show that when the output is a function the sensitivity may be measured in terms of an RKHS norm. Expand
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On the asymptotic properties of the group lasso estimator for linear models
We establish estimation and model selection consistency, pre- diction and estimation boundsand persistencefor the group-lassoestimator and model selectorproposed by Yuan and Lin (2006) for leastExpand
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Confidence sets for persistence diagrams
TLDR
Persistent homology is a method for probing topological properties of point clouds and functions. Expand
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Sparsistency of the Edge Lasso over Graphs
TLDR
The fused lasso was proposed recently to enable recovery of high-dimensional patterns which are piece-wise constant on a graph, by penalizing the ‘1-norm of dierences of measurements at vertices that share an edge. Expand
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CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS.
The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these areExpand
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Characterization of multilocus linkage disequilibrium
Linkage disequilibrium (LD) in the human genome, often measured as pairwise correlation between adjacent markers, shows substantial spatial heterogeneity. Congruent with these results, studies haveExpand
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Properties and refinements of the fused lasso
We consider estimating an unknown signal, both blocky and sparse, which is corrupted by additive noise. We study three interrelated least squares procedures and their asymptotic properties. The firstExpand
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Minimax Localization of Structural Information in Large Noisy Matrices
TLDR
We consider the problem of identifying a sparse set of relevant columns and rows in a large data matrix with highly corrupted entries. Expand
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