140 citations · 735 across the 46 of their papers we have counts for
9 papers · 1 filter
On the Pitfalls of Nested Monte Carlo
Tom Rainforth, Robert Cornish, Hongseok Yang +1
There is an increasing interest in estimating expectations outside of the classical inference framework, such as for models expressed as probabilistic programs. Many of these conte…
Inducing Interpretable Representations with Variational Autoencoders
N. Siddharth, Brooks Paige, Alban Desmaison +5
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representation…
Probabilistic structure discovery in time series data
David Janz, Brooks Paige, Tom Rainforth +2
Existing methods for structure discovery in time series data construct interpretable, compositional kernels for Gaussian process regression models. While the learned Gaussian proce…
Inference Compilation and Universal Probabilistic Programming
Tuan Anh Le, Atilim Gunes Baydin, Frank Wood
We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establish…
Design and Implementation of Probabilistic Programming Language Anglican
David Tolpin, Jan Willem van de Meent, Hongseok Yang +1
Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We introduce the programming language Anglican, outline our design cho…
Spreadsheet Probabilistic Programming
Mike Wu, Yura Perov, Frank Wood +1
Spreadsheet workbook contents are simple programs. Because of this, probabilistic programming techniques can be used to perform Bayesian inversion of spreadsheet computations. What…
Inference Networks for Sequential Monte Carlo in Graphical Models
Brooks Paige, Frank Wood
We introduce a new approach for amortizing inference in directed graphical models by learning heuristic approximations to stochastic inverses, designed specifically for use as prop…
Interacting Particle Markov Chain Monte Carlo
Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten +4
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like r…
Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
Sam Staton, Hongseok Yang, Chris Heunen +2
We study the semantic foundation of expressive probabilistic programming languages, that support higher-order functions, continuous distributions, and soft constraints (such as Ang…