Publications

Louis Tiao (2023). Probabilistic Machine Learning in the Age of Deep Learning: New Perspectives for Gaussian Processes, Bayesian Optimization and Beyond (PhD Thesis).

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Louis Tiao, Vincent Dutordoir, Victor Picheny (2023). Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes. In ICML2023. Accepted as Oral Presentation.

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Rafael Oliveira, Louis Tiao, Fabio Ramos (2022). Batch Bayesian Optimisation via Density-ratio Estimation with Guarantees. In NeurIPS2022.

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Louis Tiao, Aaron Klein, Matthias Seeger, Edwin v. Bonilla, Cédric Archambeau, Fabio Ramos (2021). BORE: Bayesian Optimization by Density-Ratio Estimation. In ICML2021. Accepted as Long Presentation (Awarded to Top 3% of Papers).

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Pantelis Elinas, Edwin v. Bonilla, Louis Tiao (2020). Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings. In NeurIPS2020. Accepted as Spotlight Presentation (Awarded to Top 3% of Papers).

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Aaron Klein, Louis Tiao, Thibaut Lienart, Cédric Archambeau, Matthias Seeger (2020). Model-based Asynchronous Hyperparameter and Neural Architecture Search.

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Louis Tiao, Edwin v. Bonilla, Fabio Ramos (2018). Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference. In ICML2018 Theoretical Foundations and Applications of Deep Generative Models. Accepted as Contributed Talk..

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