Interpretable machine learning has demonstrated impressive performance while preserving
explainability. In particular, neural additive models (NAM) offer the interpretability to the …

We consider the problem of parameter estimation in a high-dimensional generalized linear
model. Spectral methods obtained via the principal eigenvector of a suitable data …

In high dimensional variable selection problems, statisticians often seek to design multiple
testing procedures that control the False Discovery Rate (FDR), while concurrently …

In high-dimensional sparse regression, would increasing the signal-to-noise ratio while
fixing the sparsity level always lead to better model selection? For high-dimensional sparse …

The Sorted L-One Estimator (SLOPE) is a popular regularization method in regression,
which induces clustering of the estimated coefficients. That is, the estimator can have …

Sparse Group LASSO (SGL) is a regularized model for high-dimensional linear regression
problems with grouped covariates. SGL applies $ l_1 $ and $ l_2 $ penalties on the …

In the classical hypothesis testing paradigm, statisticians aim to propose testing
methodologies that effectively manage statistical significance while maximizing power, ie …

Regularized regression is a classical method for statistical estimation and learning. It has
now been successfully used in many applications including communications, biology …

In sparse linear regression, the SLOPE estimator generalizes LASSO by penalizing different
coordinates of the estimate according to their magnitudes. In this paper, we present a …

Exact asymptotic theory refers to a collection of techniques for precisely characterizing the
distribution of high-dimensional regression estimators. Examples of the estimators it …