8 citations · 16 across the 7 of their papers we have counts for
14 papers · 1 filter
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…
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.…
Neural Approximate Sufficient Statistics for Implicit Models
Yanzhi Chen, Dinghuai Zhang, Michael Gutmann +2
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractab…
Telescoping Density-Ratio Estimation
Benjamin Rhodes, Kai Xu, Michael U. Gutmann
Density-ratio estimation via classification is a cornerstone of unsupervised learning. It has provided the foundation for state-of-the-art methods in representation learning and ge…
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…