Frank Wood

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University of Oxford ∙ The University of British Columbia

Associate Professor at University of Oxford, Faculty Fellow at The Alan Turing Institute, Consultant at Invrea Limited

Deep Probabilistic Surrogate Networks for Universal Simulator Approximation

We present a framework for automatically structuring and training fast, ...
10/25/2019 ∙ by Andreas Munk, et al. ∙ 40 ∙ share

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Planning as Inference in Epidemiological Models

In this work we demonstrate how existing software tools can be used to a...
03/30/2020 ∙ by Frank Wood, et al. ∙ 34 ∙ share

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Attention for Inference Compilation

We present a new approach to automatic amortized inference in universal ...
10/25/2019 ∙ by William Harvey, et al. ∙ 21 ∙ share

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LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models

We develop a new Low-level, First-order Probabilistic Programming Langua...
03/06/2019 ∙ by Yuan Zhou, et al. ∙ 12 ∙ share

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Amortized Rejection Sampling in Universal Probabilistic Programming

Existing approaches to amortized inference in probabilistic programs wit...
10/20/2019 ∙ by Saeid Naderiparizi, et al. ∙ 11 ∙ share

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Imitation Learning of Factored Multi-agent Reactive Models

We apply recent advances in deep generative modeling to the task of imit...
03/12/2019 ∙ by Michael Teng, et al. ∙ 10 ∙ share

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Coping With Simulators That Don't Always Return

Deterministic models are approximations of reality that are easy to inte...
03/28/2020 ∙ by Andrew Warrington, et al. ∙ 6 ∙ share

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The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging

We develop a stochastic whole-brain and body simulator of the nematode r...
07/24/2019 ∙ by Andrew Warrington, et al. ∙ 5 ∙ share

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Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

We present a novel framework that enables efficient probabilistic infere...
07/20/2018 ∙ by Atilim Gunes Baydin, et al. ∙ 4 ∙ share

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Deep Variational Reinforcement Learning for POMDPs

Many real-world sequential decision making problems are partially observ...
06/06/2018 ∙ by Maximilian Igl, et al. ∙ 2 ∙ share

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Inference Trees: Adaptive Inference with Exploration

We introduce inference trees (ITs), a new class of inference methods tha...
06/25/2018 ∙ by Tom Rainforth, et al. ∙ 2 ∙ share

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An Introduction to Probabilistic Programming

This document is designed to be a first-year graduate-level introduction...
09/27/2018 ∙ by Jan-Willem van de Meent, et al. ∙ 2 ∙ share

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The Thermodynamic Variational Objective

We introduce the thermodynamic variational objective (TVO) for learning ...
06/28/2019 ∙ by Vaden Masrani, et al. ∙ 2 ∙ share

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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread atte...
07/08/2019 ∙ by Atılım Güneş Baydin, et al. ∙ 2 ∙ share

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Amortized Monte Carlo Integration

Current approaches to amortizing Bayesian inference focus solely on appr...
07/18/2019 ∙ by Adam Golinski, et al. ∙ 2 ∙ share

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Improved Few-Shot Visual Classification

Few-shot learning is a fundamental task in computer vision that carries ...
12/07/2019 ∙ by Peyman Bateni, et al. ∙ 1 ∙ share

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Updating the VESICLE-CNN Synapse Detector

We present an updated version of the VESICLE-CNN algorithm presented by ...
10/31/2017 ∙ by Andrew Warrington, et al. ∙ 0 ∙ share

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Learning Disentangled Representations with Semi-Supervised Deep Generative Models

Variational autoencoders (VAEs) learn representations of data by jointly...
06/01/2017 ∙ by N. Siddharth, et al. ∙ 0 ∙ share

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Auto-Encoding Sequential Monte Carlo

We introduce AESMC: a method for using deep neural networks for simultan...
05/29/2017 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

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Online Learning Rate Adaptation with Hypergradient Descent

We introduce a general method for improving the convergence rate of grad...
03/14/2017 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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Spreadsheet Probabilistic Programming

Spreadsheet workbook contents are simple programs. Because of this, prob...
06/14/2016 ∙ by Mike Wu, et al. ∙ 0 ∙ share

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Probabilistic structure discovery in time series data

Existing methods for structure discovery in time series data construct i...
11/21/2016 ∙ by David Janz, et al. ∙ 0 ∙ share

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Inference Compilation and Universal Probabilistic Programming

We introduce a method for using deep neural networks to amortize the cos...
10/31/2016 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

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Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints

We study the semantic foundation of expressive probabilistic programming...
01/19/2016 ∙ by Sam Staton, et al. ∙ 0 ∙ share

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Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference

In learning deep generative models, the encoder for variational inferenc...
12/01/2017 ∙ by Stefan Webb, et al. ∙ 0 ∙ share

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Using Synthetic Data to Train Neural Networks is Model-Based Reasoning

We draw a formal connection between using synthetic training data to opt...
03/02/2017 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

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Path Finding under Uncertainty through Probabilistic Inference

We introduce a new approach to solving path-finding problems under uncer...
02/25/2015 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Inducing Interpretable Representations with Variational Autoencoders

We develop a framework for incorporating structured graphical models in ...
11/22/2016 ∙ by N. Siddharth, et al. ∙ 0 ∙ share

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Inference Networks for Sequential Monte Carlo in Graphical Models

We introduce a new approach for amortizing inference in directed graphic...
02/22/2016 ∙ by Brooks Paige, et al. ∙ 0 ∙ share

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Interacting Particle Markov Chain Monte Carlo

We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...
02/16/2016 ∙ by Tom Rainforth, et al. ∙ 0 ∙ share

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Data-driven Sequential Monte Carlo in Probabilistic Programming

Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC)...
12/14/2015 ∙ by Yura N Perov, et al. ∙ 0 ∙ share

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A New Approach to Probabilistic Programming Inference

We introduce and demonstrate a new approach to inference in expressive p...
07/03/2015 ∙ by Frank Wood, et al. ∙ 0 ∙ share

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Maximum a Posteriori Estimation by Search in Probabilistic Programs

We introduce an approximate search algorithm for fast maximum a posterio...
04/26/2015 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Particle Gibbs with Ancestor Sampling for Probabilistic Programs

Particle Markov chain Monte Carlo techniques rank among current state-of...
01/27/2015 ∙ by Jan-Willem van de Meent, et al. ∙ 0 ∙ share

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Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs

We introduce an adaptive output-sensitive Metropolis-Hastings algorithm ...
01/22/2015 ∙ by David Tolpin, et al. ∙ 0 ∙ share

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Asynchronous Anytime Sequential Monte Carlo

We introduce a new sequential Monte Carlo algorithm we call the particle...
07/10/2014 ∙ by Brooks Paige, et al. ∙ 0 ∙ share

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Infinite Structured Hidden Semi-Markov Models

This paper reviews recent advances in Bayesian nonparametric techniques ...
06/30/2014 ∙ by Jonathan H. Huggins, et al. ∙ 0 ∙ share

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A Compilation Target for Probabilistic Programming Languages

Forward inference techniques such as sequential Monte Carlo and particle...
03/03/2014 ∙ by Brooks Paige, et al. ∙ 0 ∙ share

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Tempering by Subsampling

In this paper we demonstrate that tempering Markov chain Monte Carlo sam...
01/28/2014 ∙ by Jan-Willem van de Meent, et al. ∙ 0 ∙ share

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Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data

We address the problem of analyzing sets of noisy time-varying signals t...
05/15/2013 ∙ by Jan-Willem van de Meent, et al. ∙ 0 ∙ share

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Inferring Team Strengths Using a Discrete Markov Random Field

We propose an original model for inferring team strengths using a Markov...
05/09/2013 ∙ by John Zech, et al. ∙ 0 ∙ share

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A Non-Parametric Bayesian Method for Inferring Hidden Causes

We present a non-parametric Bayesian approach to structure learning with...
06/27/2012 ∙ by Frank Wood, et al. ∙ 0 ∙ share

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Inference in Hidden Markov Models with Explicit State Duration Distributions

In this letter we borrow from the inference techniques developed for unb...
02/29/2012 ∙ by Michael Dewar, et al. ∙ 0 ∙ share

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Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures

This paper proposes a technique for the unsupervised detection and track...
10/11/2012 ∙ by Willie Neiswanger, et al. ∙ 0 ∙ share

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Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

We consider the problem of Bayesian inference in the family of probabili...
12/21/2017 ∙ by Mario Lezcano Casado, et al. ∙ 0 ∙ share

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Bayesian Optimization for Probabilistic Programs

We present the first general purpose framework for marginal maximum a po...
07/13/2017 ∙ by Tom Rainforth, et al. ∙ 0 ∙ share

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Tighter Variational Bounds are Not Necessarily Better

We provide theoretical and empirical evidence that using tighter evidenc...
02/13/2018 ∙ by Tom Rainforth, et al. ∙ 0 ∙ share

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On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators

We present a formalization of nested Monte Carlo (NMC) estimation, where...
09/18/2017 ∙ by Tom Rainforth, et al. ∙ 0 ∙ share

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High Throughput Synchronous Distributed Stochastic Gradient Descent

We introduce a new, high-throughput, synchronous, distributed, data-para...
03/12/2018 ∙ by Michael Teng, et al. ∙ 0 ∙ share

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Revisiting Reweighted Wake-Sleep

Discrete latent-variable models, while applicable in a variety of settin...
05/26/2018 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

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Frank Wood

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