495 citations · 932 across the 37 of their papers we have counts for
40 papers
An Extendable Python Implementation of Robust Optimisation Monte Carlo
Vasilis Gkolemis, Michael Gutmann, Henri Pesonen
Performing inference in statistical models with an intractable likelihood is challenging, therefore, most likelihood-free inference (LFI) methods encounter accuracy and efficiency…
Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling
Vaidotas Simkus, Michael U. Gutmann
Conditional sampling of variational autoencoders (VAEs) is needed in various applications, such as missing data imputation, but is computationally intractable. A principled choice…
Designing Optimal Behavioral Experiments Using Machine Learning
Simon Valentin, Steven Kleinegesse, Neil R. Bramley +3
Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely, and offer predictions that can be su…
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
Akash Srivastava, Seungwook Han, Kai Xu +2
Functions of the ratio of the densities are widely used in machine learning to quantify the discrepancy between the two distributions and . For high-dimensional distri…
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…