“...We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a...”

“...In many fields of science, we observe a response variable together with a large number of potential explanatory variables, and would like to be able to...”

“...We analyze the performance of spectral clustering for community extraction in stochastic block models. We show that, under mild conditions, spectral clustering...”

“...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...”

“...We present a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing...”

“...We propose a general method for constructing confidence intervals and statistical tests for single or low-dimensional components of a large parameter vector in...”

“...We study full Bayesian procedures for high-dimensional linear regression under sparsity constraints. The prior is a mixture of point masses at zero and...”

“...Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, representing...”

“...Most high-dimensional estimation methods propose to minimize a cost function (empirical risk) that is a sum of losses associated to each data point (each...”