Machine Learning

BORE: Bayesian Optimization by Density-Ratio Estimation

Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are derived from the …

A Primer on Pólya-gamma Random Variables - Part II: Bayesian Logistic Regression

One weird trick to make exact inference in Bayesian logistic regression tractable.

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.

BORE: Bayesian Optimization by Density-Ratio Estimation

Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods. BO proposes solutions according to an explore-exploit trade-off criterion encoded in an acquisition function, many of which are derived from the …

Progress Review 2020

PhD candidature annual progress review for 2019-2020.

Progress Review 2021

PhD candidature annual progress review for 2020-2021.

AutoGluon

AutoGluon is a library that implements numerous state-of-the-art methods for asynchronously distributed hyperparameter optimization (HPO) and neural architecture search (NAS). I was a core developer of the [Gaussian process-based multi-fidelity searcher](https://autogluon.mxnet.io/api/autogluon.searcher.html#gpmultifidelitysearcher) module.

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a …

Model-based Asynchronous Hyperparameter and Neural Architecture Search

We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization. At the heart of our method is a …

Variational Graph Convolutional Networks

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a …