4 papers · 1 filter
Optimal structure learning and conditional independence testing
Ming Gao, Yuhao Wang, Bryon Aragam
We establish a fundamental connection between optimal structure learning and optimal conditional independence testing by showing that the minimax optimal rate for structure learnin…
KL-BSS: Rethinking optimality for neighbourhood selection in structural equation models
Ming Gao, Wai Ming Tai, Bryon Aragam
We introduce a new method for neighbourhood selection in linear structural equation models that improves over classical methods such as best subset selection (BSS) and the Lasso. O…
Learning general conditional independence structures via the neighbourhood lattice
Arash A. Amini, Bryon Aragam, Qing Zhou
We study the problem of learning multivariate dependencies in nonparametric and high-dimensional settings. This includes but is not limited to graphical models. Our approach effect…
Optimality and computational barriers in variable selection under dependence
Ming Gao, Bryon Aragam
We study the optimal sample complexity of variable selection in linear regression under general design covariance, and show that subset selection is optimal while under standard co…