7 citations · 8 across the 3 of their papers we have counts for
7 papers
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…
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
Desi R. Ivanova, Adam Foster, Steven Kleinegesse +2
We introduce implicit Deep Adaptive Design (iDAD), a new method for performing adaptive experiments in real-time with implicit models. iDAD amortizes the cost of Bayesian optimal e…
Bayesian Optimal Experimental Design for Simulator Models of Cognition
Simon Valentin, Steven Kleinegesse, Neil R. Bramley +2
Bayesian optimal experimental design (BOED) is a methodology to identify experiments that are expected to yield informative data. Recent work in cognitive science considered BOED f…
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
Steven Kleinegesse, Michael U. Gutmann
We introduce a framework for Bayesian experimental design (BED) with implicit models, where the data-generating distribution is intractable but sampling from it is still possible.…
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
Steven Kleinegesse, Christopher Drovandi, Michael U. Gutmann
Bayesian experimental design (BED) is a framework that uses statistical models and decision making under uncertainty to optimise the cost and performance of a scientific experiment…
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Steven Kleinegesse, Michael U. Gutmann
Implicit stochastic models, where the data-generation distribution is intractable but sampling is possible, are ubiquitous in the natural sciences. The models typically have free p…