Paper-Conference

Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for …
Louis Tiao, Vincent Dutordoir, Victor Picheny
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Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Batch Bayesian Optimisation via Density-ratio Estimation with Guarantees
We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given …
Rafael Oliveira, Louis Tiao, Fabio Ramos
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BORE: Bayesian Optimization by Density-Ratio Estimation
We reformulate the computation of the acquisition function in Bayesian optimization (BO) as a probabilistic classification problem, providing advantages in scalability, flexibility, and representational capacity, while casting aside the limitations of tractability constraints on the model.
Louis Tiao, Aaron Klein, Matthias Seeger, Edwin v. Bonilla, Cédric Archambeau, Fabio Ramos
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BORE: Bayesian Optimization by Density-Ratio Estimation
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
Our proposed framework uses a joint probabilistic model and stochastic variational inference to improve the performance and robustness of graph convolutional networks (GCNs) in scenarios without input graph data, outperforming state-of-the-art algorithms on semi-supervised classification tasks.
Pantelis Elinas, Edwin v. Bonilla, Louis Tiao
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Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings