Machine Learning

This thesis explores the intersection of deep learning and probabilistic machine learning to enhance the capabilities of artificial intelligence. It addresses the limitations of Gaussian processes (GPs) in practical applications, particularly in comparison to neural networks (NNs), and proposes advancements such as improved approximations and a novel formulation of Bayesian optimization (BO) that seamlessly integrates deep learning methods. The contributions aim to enrich the interplay between deep learning and probabilistic ML, advancing the foundations of AI and fostering the development of more capable and reliable automated decision-making systems.

Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for …

A short and practical guide to efficiently computing the Cholesky decomposition of matrices perturbed by low-rank updates

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given …

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.

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

A short illustrated reference guide to the Knowledge Gradient acquisition function with an implementation from scratch in TensorFlow Probability.

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.

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.

A summary of notation, identities and derivations for the sparse variational Gaussian process (SVGP) framework.