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.
Louis Tiao
Research Scientist
My name is Louis Tiao, and I graduated from one of Australia’s
top engineering schools with really good grades.
Now, I’m using my knowledge to help up-and-coming tech companies
make it in this competitive world.
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.
Contributed Talk: BORE — Bayesian Optimization by Density-Ratio Estimation
NeurIPS 2020 4th Workshop on Meta-Learning (virtual).
Bayesian Optimization by Density Ratio Estimation
📄 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 …
Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings
We propose a joint probabilistic model with stochastic variational inference to improve the performance and robustness of graph convolutional networks (GCNs) in scenarios without …
Pantelis Elinas
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 …
Aaron Klein
Variational Graph Convolutional Networks
A Handbook for Sparse Variational Gaussian Processes
We summarize the notation, identities, and derivations underlying the sparse variational Gaussian process (SVGP) framework.