33 citations · 33 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision
Pantelis Elinas, Edwin V. Bonilla
Learning useful node and graph representations with graph neural networks (GNNs) is a challenging task. It is known that deep GNNs suffer from over-smoothing where, as the number o…
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Pantelis Elinas, Edwin V. Bonilla, Louis Tiao
We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial a…