216 citations · 454 across the 55 of their papers we have counts for
6 papers · 1 filter
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…
Tradeoffs of Linear Mixed Models in Genome-wide Association Studies
Haohan Wang, Bryon Aragam, Eric Xing
Motivated by empirical arguments that are well-known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) a…
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
Ben Lengerich, Caleb Ellington, Bryon Aragam +2
Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) identify context-dependent relationships between variables, but the non-convexity induced by the acyclicity…
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Goutham Rajendran, Bohdan Kivva, Ming Gao +1
Greedy algorithms have long been a workhorse for learning graphical models, and more broadly for learning statistical models with sparse structure. In the context of learning direc…
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…
Learning latent causal graphs via mixture oracles
Bohdan Kivva, Goutham Rajendran, Pradeep Ravikumar +1
We study the problem of reconstructing a causal graphical model from data in the presence of latent variables. The main problem of interest is recovering the causal structure over…