495 citations · 932 across the 37 of their papers we have counts for
4 papers · 1 filter
Bayesian Optimization with Informative Covariance
Afonso Eduardo, Michael U. Gutmann
Bayesian optimization is a methodology for global optimization of unknown and expensive objectives. It combines a surrogate Bayesian regression model with an acquisition function t…
Enhanced gradient-based MCMC in discrete spaces
Benjamin Rhodes, Michael Gutmann
The recent introduction of gradient-based MCMC for discrete spaces holds great promise, and comes with the tantalising possibility of new discrete counterparts to celebrated contin…
Pen and Paper Exercises in Machine Learning
Michael U. Gutmann
This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, un…
Statistical applications of contrastive learning
Michael U. Gutmann, Steven Kleinegesse, Benjamin Rhodes
The likelihood function plays a crucial role in statistical inference and experimental design. However, it is computationally intractable for several important classes of statistic…