Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference. - Louis C. Tiao; Edwin V. Bonilla; Fabio Ramos 001 (CoRR, 2018)

Variational Spectral Graph Convolutional Networks. - Louis C. Tiao; Pantelis Elinas; Harrison Nguyen; Edwin V. Bonilla (CoRR, 2019)

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. - Pantelis Elinas; Edwin V. Bonilla; Louis C. Tiao (NeurIPS, 2020)

Model-based Asynchronous Hyperparameter Optimization. - Louis C. Tiao; Aaron Klein; Cédric Archambeau; Matthias W. Seeger (CoRR, 2020)

BORE: Bayesian Optimization by Density-Ratio Estimation. - Louis C. Tiao; Aaron Klein; Matthias W. Seeger; Edwin V. Bonilla; Cédric Archambeau; Fabio Ramos 001 (ICML, 2021)

Batch Bayesian optimisation via density-ratio estimation with guarantees. - Rafael Oliveira 001; Louis C. Tiao; Fabio T. Ramos 001 (NeurIPS, 2022)

Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes. - Louis C. Tiao; Vincent Dutordoir; Victor Picheny (ICML, 2023)