15 citations · 27 across the 4 of their papers we have counts for
5 papers
Simulation-Based Inference for Global Health Decisions
Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli +8
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision m…
Amortized Rejection Sampling in Universal Probabilistic Programming
Saeid Naderiparizi, Adam Ścibior, Andreas Munk +9
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. This is particularly true of importance sampli…
Faithful Inversion of Generative Models for Effective Amortized Inference
Stefan Webb, Adam Golinski, Robert Zinkov +4
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently. Generally, they require the inversi…
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov +1
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a…
Composing inference algorithms as program transformations
Robert Zinkov, Chung-chieh Shan
Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm. We reduce this effort by generating inference p…