46 citations · 294 across the 26 of their papers we have counts for
26 papers
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…
Sequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scores
Gabriel Ruiz, Oscar Hernan Madrid Padilla, Qing Zhou
Causal discovery, the learning of causality in a data mining scenario, has been of strong scientific and theoretical interest as a starting point to identify "what causes what?" Co…
Distributed Learning of Generalized Linear Causal Networks
Qiaoling Ye, Arash A. Amini, Qing Zhou
We consider the task of learning causal structures from data stored on multiple machines, and propose a novel structure learning method called distributed annealing on regularized…
Partitioned hybrid learning of Bayesian network structures
Jireh Huang, Qing Zhou
We develop a novel hybrid method for Bayesian network structure learning called partitioned hybrid greedy search (pHGS), composed of three distinct yet compatible new algorithms: P…
An empirical Bayes Approach to stochastic blockmodels and graphons: shrinkage estimation and model selection
Zhanhao Peng, Qing Zhou
The graphon (W-graph), including the stochastic block model as a special case, has been widely used in modeling and analyzing network data. This random graph model is well-characte…
Causal network learning with non-invertible functional relationships
Bingling Wang, Qing Zhou
Discovery of causal relationships from observational data is an important problem in many areas. Several recent results have established the identifiability of causal DAGs with non…