8 citations · 27 across the 9 of their papers we have counts for
9 papers
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…
Domain Knowledge in A*-Based Causal Discovery
Steven Kleinegesse, Andrew R. Lawrence, Hana Chockler
Causal discovery has become a vital tool for scientists and practitioners wanting to discover causal relationships from observational data. While most previous approaches to causal…
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…
Efficient Bayesian Experimental Design for Implicit Models
Steven Kleinegesse, Michael Gutmann
Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance. For implicit models, where the likeliho…