Louis Tiao
Louis Tiao
Home
Publications
Projects
Talks
Posts
Contact
CV
Light
Dark
Automatic
Machine Learning
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
PDF
Cite
Code
Poster
Slides
Video
Conference Proceeding
Supplementary material
Efficient Cholesky decomposition of low-rank updates
A short and practical guide to efficiently computing the Cholesky decomposition of matrices perturbed by low-rank updates
Louis Tiao
Last updated on Apr 19, 2023
5 min read
technical
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
PDF
Code
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
PDF
Cite
Code
Poster
Slides
Video
Conference Proceeding
Supplementary material
A Primer on Pólya-gamma Random Variables - Part II: Bayesian Logistic Regression
One weird trick to make exact inference in Bayesian logistic regression tractable.
Louis Tiao
Last updated on Oct 16, 2022
21 min read
technical
An Illustrated Guide to the Knowledge Gradient Acquisition Function
A short illustrated reference guide to the Knowledge Gradient acquisition function with an implementation from scratch in TensorFlow Probability.
Louis Tiao
Last updated on Oct 22, 2022
7 min read
technical
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
PDF
Cite
Code
Dataset
Video
Model-based Asynchronous Hyperparameter and Neural Architecture Search
We introduce a model-based method for asynchronous multi-fidelity hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization, achieving substantial speed-ups over current state-of-the-art methods on challenging benchmarks for tabular data, image classification, and language modeling.
Aaron Klein
,
Louis Tiao
,
Thibaut Lienart
,
Cédric Archambeau
,
Matthias Seeger
PDF
Cite
Code
Video
A Handbook for Sparse Variational Gaussian Processes
A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.
Louis Tiao
Last updated on Oct 19, 2022
21 min read
technical
Density Ratio Estimation for KL Divergence Minimization between Implicit Distributions
This post demonstrates how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions, using density ratio estimation by probabilistic classification.
Louis Tiao
Last updated on Oct 16, 2022
21 min read
technical
»
Cite
×