We give safe screening rules to eliminate variables from regression with $\ell_0 $
regularization or cardinality constraint. These rules are based on guarantees that a feature …

Signal estimation problems with smoothness and sparsity priors can be naturally modeled
as quadratic optimization with ℓ0-" norm" constraints. Since such problems are nonconvex …

Sparse regression models are increasingly prevalent due to their ease of interpretability and
superior out-of-sample performance. However, the exact model of sparse regression with an …

We introduce a new library named abess that implements a unified framework of best-subset
selection for solving diverse machine learning problems, eg, linear regression, classification …

This paper develops a correspondence relating convex hulls of fractional functions with
those of polynomial functions over the same domain. Using this result, we develop a number …

We consider the problem of learning an optimal prescriptive tree (ie, an interpretable
treatment assignment policy in the form of a binary tree) of moderate depth, from …

Subset selection for multiple linear regression aims to construct a regression model that
minimizes errors by selecting a small number of explanatory variables. Once a model is …

Canonical correlation analysis (CCA) is a family of multivariate statistical methods for
extracting mutual information contained in multiple datasets. To improve the interpretability …

We study the feature-based newsvendor problem, in which a decision-maker has access to
historical data consisting of demand observations and exogenous features. In this setting …

In this paper we consider two relevant optimization problems: the problem of selecting the
best sparse linear regression model and the problem of optimally identifying the parameters …