
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Achieving the full promise of the Thermodynamic Variational Objective (T...
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Uncertainty in Neural Processes
We explore the effects of architecture and training objective choice on ...
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Assisting the Adversary to Improve GAN Training
We propose a method for improved training of generative adversarial netw...
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All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
The recently proposed Thermodynamic Variational Objective (TVO) leverage...
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Semisupervised Sequential Generative Models
We introduce a novel objective for training deep generative timeseries ...
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Improving FewShot Visual Classification with Unlabelled Examples
We propose a transductive metalearning method that uses unlabelled inst...
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Planning as Inference in Epidemiological Models
In this work we demonstrate how existing software tools can be used to a...
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Coping With Simulators That Don't Always Return
Deterministic models are approximations of reality that are easy to inte...
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Improved FewShot Visual Classification
Fewshot learning is a fundamental task in computer vision that carries ...
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Attention for Inference Compilation
We present a new approach to automatic amortized inference in universal ...
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Deep Probabilistic Surrogate Networks for Universal Simulator Approximation
We present a framework for automatically structuring and training fast, ...
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Amortized Rejection Sampling in Universal Probabilistic Programming
Existing approaches to amortized inference in probabilistic programs wit...
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Safer EndtoEnd Autonomous Driving via Conditional Imitation Learning and Command Augmentation
Imitation learning is a promising approach to endtoend training of aut...
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The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging
We develop a stochastic wholebrain and body simulator of the nematode r...
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Amortized Monte Carlo Integration
Current approaches to amortizing Bayesian inference focus solely on appr...
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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread atte...
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The Thermodynamic Variational Objective
We introduce the thermodynamic variational objective (TVO) for learning ...
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NearOptimal Glimpse Sequences for Improved Hard Attention Neural Network Training
We introduce the use of Bayesian optimal experimental design techniques ...
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Imitation Learning of Factored Multiagent Reactive Models
We apply recent advances in deep generative modeling to the task of imit...
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LFPPL: A LowLevel First Order Probabilistic Programming Language for NonDifferentiable Models
We develop a new Lowlevel, Firstorder Probabilistic Programming Langua...
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An Introduction to Probabilistic Programming
This document is designed to be a firstyear graduatelevel introduction...
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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...
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Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods tha...
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Deep Variational Reinforcement Learning for POMDPs
Many realworld sequential decision making problems are partially observ...
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Revisiting Reweighted WakeSleep
Discrete latentvariable models, while applicable in a variety of settin...
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Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs
Hamiltonian Monte Carlo (HMC) is the dominant statistical inference algo...
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High Throughput Synchronous Distributed Stochastic Gradient Descent
We introduce a new, highthroughput, synchronous, distributed, datapara...
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Tighter Variational Bounds are Not Necessarily Better
We provide theoretical and empirical evidence that using tighter evidenc...
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Improvements to Inference Compilation for Probabilistic Programming in LargeScale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabili...
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Faithful Model Inversion Substantially Improves Autoencoding Variational Inference
In learning deep generative models, the encoder for variational inferenc...
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Updating the VESICLECNN Synapse Detector
We present an updated version of the VESICLECNN algorithm presented by ...
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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...
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Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a po...
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Learning Disentangled Representations with SemiSupervised Deep Generative Models
Variational autoencoders (VAEs) learn representations of data by jointly...
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AutoEncoding Sequential Monte Carlo
We introduce AESMC: a method for using deep neural networks for simultan...
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Online Learning Rate Adaptation with Hypergradient Descent
We introduce a general method for improving the convergence rate of grad...
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Using Synthetic Data to Train Neural Networks is ModelBased Reasoning
We draw a formal connection between using synthetic training data to opt...
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Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in ...
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Probabilistic structure discovery in time series data
Existing methods for structure discovery in time series data construct i...
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Inference Compilation and Universal Probabilistic Programming
We introduce a method for using deep neural networks to amortize the cos...
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Spreadsheet Probabilistic Programming
Spreadsheet workbook contents are simple programs. Because of this, prob...
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Inference Networks for Sequential Monte Carlo in Graphical Models
We introduce a new approach for amortizing inference in directed graphic...
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Interacting Particle Markov Chain Monte Carlo
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...
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Semantics for probabilistic programming: higherorder functions, continuous distributions, and soft constraints
We study the semantic foundation of expressive probabilistic programming...
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Datadriven Sequential Monte Carlo in Probabilistic Programming
Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC)...
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A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive p...
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Maximum a Posteriori Estimation by Search in Probabilistic Programs
We introduce an approximate search algorithm for fast maximum a posterio...
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Path Finding under Uncertainty through Probabilistic Inference
We introduce a new approach to solving pathfinding problems under uncer...
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Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Particle Markov chain Monte Carlo techniques rank among current stateof...
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OutputSensitive Adaptive MetropolisHastings for Probabilistic Programs
We introduce an adaptive outputsensitive MetropolisHastings algorithm ...
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Frank Wood
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Associate Professor at University of Oxford, Faculty Fellow at The Alan Turing Institute, Consultant at Invrea Limited