495 citations · 932 across the 37 of their papers we have counts for
4 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…
Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data
Vaidotas Simkus, Benjamin Rhodes, Michael U. Gutmann
Statistical models are central to machine learning with broad applicability across a range of downstream tasks. The models are controlled by free parameters that are typically esti…
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.…