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Consistency of spectral clustering in stochastic block models
TLDR
It is shown that, under mild conditions, spectral clustering applied to the adjacency matrix of the network can consistently recover hidden communities even when the order of the maximum expected degree is as small as $\log n$ with $n$ the number of nodes.
Distribution-Free Predictive Inference for Regression
TLDR
A general framework for distribution-free predictive inference in regression, using conformal inference, which allows for the construction of a prediction band for the response variable using any estimator of the regression function, and a model-free notion of variable importance, called leave-one-covariate-out or LOCO inference.
Differential privacy for functions and functional data
TLDR
This work shows that adding an appropriate Gaussian process to the function of interest yields differential privacy, and develops methods for releasing functions while preserving differential privacy.
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 least
Confidence sets for persistence diagrams
TLDR
This paper derives confidence sets that allow us to separate topological signal from topological noise, and brings some statistical ideas to persistent homology.
Properties and refinements of the fused lasso
TLDR
This work derives conditions for the recovery of the true block partition and the true sparsity patterns by the fused lasso and the fused adaptive lasso, and derives convergence rates for the sieve estimators, explicitly in terms of the constraining parameters.
CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS.
TLDR
It is shown that this apparently trivial condition is in fact violated by many popular and scientifically appealing models, and that satisfying it drastically limits ERGM's expressive power.
Sparsistency of the Edge Lasso over Graphs
TLDR
This paper investigates sparsistency of fused lasso for general graph structures, i.e. its ability to correctly recover the exact support of piece-wise constant graphstructured patterns asymptotically (for largescale graphs) and refers to it as Edge Lasso on the (structured) normal means setting.
Characterization of multilocus linkage disequilibrium
TLDR
A new method is proposed that aims to distinguish between blocked and unblocked regions in the genome, and a new, efficient method to select SNPs for association analysis, namely tag SNPs is proposed.
Autoregressive process modeling via the Lasso procedure
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