We evaluate a large-scale set of interventions to increase demand for immunization in
Haryana, India. The policies under consideration include the two most frequently discussed …

Hyperspectral imaging is a powerful technology for remotely inferring the material properties
of the objects in a scene of interest. Hyperspectral images consist of spatial maps of light …

We propose a methodology for testing linear hypothesis in high-dimensional linear models.
The proposed test does not impose any restriction on the size of the model, that is, model …

We experimentally study the impact of relatively large enterprise loans in Egypt. Larger
loans generate small average impacts, but machine learning using psychometric data …

Sparse linear regression is a central problem in high-dimensional statistics. We study the
correlated random design setting, where the covariates are drawn from a multivariate …

Sparse linear regression is a fundamental problem in high-dimensional statistics, but
strikingly little is known about how to efficiently solve it without restrictive conditions on the …

This work presents a data-driven approach to the identification of spatial and temporal
truncation errors for linear and nonlinear discretization schemes of Partial Differential …

Standard high-dimensional regression methods assume that the underlying coefficient
vector is sparse. This might not be true in some cases, in particular in presence of hidden …

Phase retrieval in optical imaging refers to the recovery of a complex signal from phaseless
data acquired in the form of its diffraction patterns. These patterns are acquired through a …

Accurately predicting faulty software units helps practitioners target faulty units and prioritize
their efforts to maintain software quality. Prior studies use machine-learning models to detect …