This paper investigates the asymptotic distribution of the maximum-likelihood estimate
(MLE) in multinomial logistic models in the high-dimensional regime where dimension and …

Estimation of the average treatment effect (ATE) is a central problem in causal inference. In
recent times, inference for the ATE in the presence of high-dimensional covariates has been …

We consider observations $(X, y) $ from single index models with unknown link function,
Gaussian covariates and a regularized M-estimator $\hat\beta $ constructed from convex …

We study semi-parametric estimation of the population mean when data is observed missing
at random (MAR) in the $ n< p $" inconsistency regime", in which neither the outcome model …

Direct use of the likelihood function typically produces severely biased estimates when the
dimension of the parameter vector is large relative to the effective sample size. With linearly …

Identifying the relevant variables for a classification model with correct confidence levels is a
central but difficult task in high-dimension. Despite the core role of sparse logistic regression …

This study proposes a novel method for estimation and hypothesis testing in high-
dimensional single-index models. We address a common scenario where the sample size …

We revisit the classical causal inference problem of estimating the average treatment effect
in the presence of fully observed confounding variables using two-stage semiparametric …

We consider the problem of estimating a low-dimensional parameter in high-dimensional
linear regression. Constructing an approximately unbiased estimate of the parameter of …

In the last decade, many excellent active learning methods have been proposed whose
algorithms generally deliver an acceptable performance. However, many of these methods …