83 citations · 225 across the 27 of their papers we have counts for
4 papers · 1 filter
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
Rui Zhang, Christian J. Walder, Edwin V. Bonilla +2
Approximate inference techniques are the cornerstone of probabilistic methods based on Gaussian process priors. Despite this, most work approximately optimizes standard divergence…
Structured Variational Inference in Continuous Cox Process Models
Virginia Aglietti, Edwin V. Bonilla, Theodoros Damoulas +1
We propose a scalable framework for inference in an inhomogeneous Poisson process modeled by a continuous sigmoidal Cox process that assumes the corresponding intensity function is…
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…
Scalable Grouped Gaussian Processes via Direct Cholesky Functional Representations
Astrid Dahl, Edwin V. Bonilla
We consider multi-task regression models where observations are assumed to be a linear combination of several latent node and weight functions, all drawn from Gaussian process (GP)…