12 citations · 14 across the 5 of their papers we have counts for
5 papers
Bayesian Program Learning by Decompiling Amortized Knowledge
Alessandro B. Palmarini, Christopher G. Lucas, N. Siddharth
DreamCoder is an inductive program synthesis system that, whilst solving problems, learns to simplify search in an iterative wake-sleep procedure. The cost of search is amortized b…
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…
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…
Universal Marginaliser for Deep Amortised Inference for Probabilistic Programs
Robert Walecki, Kostis Gourgoulias, Adam Baker +7
Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its inherent uncertainty. Inference…
A Universal Marginalizer for Amortized Inference in Generative Models
Laura Douglas, Iliyan Zarov, Konstantinos Gourgoulias +6
We consider the problem of inference in a causal generative model where the set of available observations differs between data instances. We show how combining samples drawn from t…