1 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
Rényi Neural Processes
Xuesong Wang, He Zhao, Edwin V. Bonilla
Neural Processes (NPs) are deep probabilistic models that represent stochastic processes by conditioning their prior distributions on a set of context points. Despite their advanta…
ProDAG: Projected Variational Inference for Directed Acyclic Graphs
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
Directed acyclic graph (DAG) learning is a central task in structure discovery and causal inference. Although the field has witnessed remarkable advances over the past few years, i…
Optimal Transport for Structure Learning Under Missing Data
Vy Vo, He Zhao, Trung Le +2
Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma. While the goal is to recover the true causal structure, robust imputation requires considerin…
Bayesian Vector AutoRegression with Factorised Granger-Causal Graphs
He Zhao, Vassili Kitsios, Terence J. O'Kane +1
We study the problem of automatically discovering Granger causal relations from observational multivariate time-series data.Vector autoregressive (VAR) models have been time-tested…
Permutation-based Inference for Variational Learning of Directed Acyclic Graphs
Edwin V. Bonilla, Pantelis Elinas, He Zhao +3
Estimating the structure of Bayesian networks as directed acyclic graphs (DAGs) from observational data is a fundamental challenge, particularly in causal discovery. Bayesian appro…