Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …

In exciting new work, Bertsimas et al.(2016) showed that the classical best subset selection
problem in regression modeling can be formulated as a mixed integer optimization (MIO) …

In this paper, we provide efficient estimators and honest confidence bands for a variety of
treatment effects including local average (LATE) and local quantile treatment effects (LQTE) …

Convolutional neural networks (CNN) have led to many state-of-the-art results spanning
through various fields. However, a clear and profound theoretical understanding of the …

A general theory of hypothesis tests and confidence regions for sparse high dimensional
models Page 1 The Annals of Statistics 2017, Vol. 45, No. 1, 158–195 DOI: 10.1214/16-AOS1448 …

Curating labeled training data has become the primary bottleneck in machine learning.
Recent frameworks address this bottleneck with generative models to synthesize labels at …

Supplement to “Confidence intervals for high-dimensional linear regression: Minimax rates
and adaptivity”. Detailed proofs of the adaptivity lower bound and minimax upper bound for …

This chapter reviews the instrumental variable quantile regression model of Chernozhukov
and Hansen. It discusses the key conditions used for identification of structural quantile …

Support recovery without incoherence: A case for nonconvex regularization Page 1 The Annals
of Statistics 2017, Vol. 45, No. 6, 2455–2482 https://doi.org/10.1214/16-AOS1530 © Institute …