Efficient Cholesky decomposition of low-rank updates
We give a short and practical guide to efficiently computing the Cholesky decomposition of matrices perturbed by low-rank updates.
📄 One paper accepted to NeurIPS 2022
Our paper "Batch Bayesian Optimisation via Density-ratio Estimation with Guarantees", led by Rafael Oliveira, was paper accepted to NeurIPS2022!
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A Primer on Pólya-gamma Random Variables - Part III: Local Variational Methods
We swap Gibbs sampling for mean-field variational inference in the Pólya-Gamma augmented model and watch the classical Jaakkola-Jordan bound on the logistic sigmoid fall out, EM …
📄 One paper accepted to ICML 2021
Our paper "BORE — Bayesian Optimization by Density-Ratio Estimation" was accepted to ICML 2021 as a Long Talk (top 3% of submissions).
A Primer on Pólya-gamma Random Variables - Part II: Bayesian Logistic Regression
We use one weird trick — Pólya-Gamma augmentation — to make exact inference in Bayesian logistic regression tractable.
A Primer on Pólya-gamma Random Variables - Part I: Basic Relationships
We collect the identities that make the Pólya-Gamma augmentation tick: the logistic sigmoid in terms of the hyperbolic cosine, the hyperbolic cosine as a Pólya-Gamma Laplace …
An Illustrated Guide to the Knowledge Gradient Acquisition Function
We give a short illustrated reference guide to the Knowledge Gradient acquisition function with an implementation from scratch in TensorFlow Probability.
📄 One paper accepted to NeurIPS 2020
Our paper "Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings" was accepted to NeurIPS 2020 as a Spotlight Presentation …
A Handbook for Sparse Variational Gaussian Processes
We summarize the notation, identities, and derivations underlying the sparse variational Gaussian process (SVGP) framework.
Density Ratio Estimation for KL Divergence Minimization between Implicit Distributions
We show how to approximate the KL divergence (in fact, any f-divergence) between implicit distributions using density ratio estimation by probabilistic classification.