216 citations · 454 across the 55 of their papers we have counts for
12 papers · 1 filter
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…
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…
Inconsistency of cross-validation for structure learning in Gaussian graphical models
Zhao Lyu, Wai Ming Tai, Mladen Kolar +1
Despite numerous years of research into the merits and trade-offs of various model selection criteria, obtaining robust results that elucidate the behavior of cross-validation rema…
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…
Optimal estimation of Gaussian DAG models
Ming Gao, Wai Ming Tai, Bryon Aragam
We study the optimal sample complexity of learning a Gaussian directed acyclic graph (DAG) from observational data. Our main results establish the minimax optimal sample complexity…
Efficient Bayesian network structure learning via local Markov boundary search
Ming Gao, Bryon Aragam
We analyze the complexity of learning directed acyclic graphical models from observational data in general settings without specific distributional assumptions. Our approach is inf…
Uniform Consistency in Nonparametric Mixture Models
Bryon Aragam, Ruiyi Yang
We study uniform consistency in nonparametric mixture models as well as closely related mixture of regression (also known as mixed regression) models, where the regression function…
On perfectness in Gaussian graphical models
Arash A. Amini, Bryon Aragam, Qing Zhou
Knowing when a graphical model is perfect to a distribution is essential in order to relate separation in the graph to conditional independence in the distribution, and this is par…
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
Bryon Aragam, Chen Dan, Eric P. Xing +1
Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable, by introducing a novel framework in…
The neighborhood lattice for encoding partial correlations in a Hilbert space
Arash A. Amini, Bryon Aragam, Qing Zhou
Neighborhood regression has been a successful approach in graphical and structural equation modeling, with applications to learning undirected and directed graphical models. We ext…
Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression
Bryon Aragam, Arash A. Amini, Qing Zhou
We study a family of regularized score-based estimators for learning the structure of a directed acyclic graph (DAG) for a multivariate normal distribution from high-dimensional da…