140 citations · 735 across the 46 of their papers we have counts for
49 papers
Uncertain Evidence in Probabilistic Models and Stochastic Simulators
Andreas Munk, Alexander Mead, Frank Wood
We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred to as "uncertain evidence." We explore…
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation
Adam Scibior, Vasileios Lioutas, Daniele Reda +2
We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction. Agents are modeled with conditional recurrent variational neural…
Annealed Importance Sampling with q-Paths
Rob Brekelmans, Vaden Masrani, Thang Bui +4
Annealed importance sampling (AIS) is the gold standard for estimating partition functions or marginal likelihoods, corresponding to importance sampling over a path of distribution…
Robust Asymmetric Learning in POMDPs
Andrew Warrington, J. Wilder Lavington, Adam Ścibior +2
Policies for partially observed Markov decision processes can be efficiently learned by imitating policies for the corresponding fully observed Markov decision processes. Unfortuna…
Ensemble Squared: A Meta AutoML System
Jason Yoo, Tony Joseph, Dylan Yung +2
There are currently many barriers that prevent non-experts from exploiting machine learning solutions ranging from the lack of intuition on statistical learning techniques to the t…
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
Vu Nguyen, Vaden Masrani, Rob Brekelmans +2
Achieving the full promise of the Thermodynamic Variational Objective (TVO), a recently proposed variational lower bound on the log evidence involving a one-dimensional Riemann int…
Uncertainty in Neural Processes
Saeid Naderiparizi, Kenny Chiu, Benjamin Bloem-Reddy +1
We explore the effects of architecture and training objective choice on amortized posterior predictive inference in probabilistic conditional generative models. We aim this work to…
Assisting the Adversary to Improve GAN Training
Andreas Munk, William Harvey, Frank Wood
Some of the most popular methods for improving the stability and performance of GANs involve constraining or regularizing the discriminator. In this paper we consider a largely ove…
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
Rob Brekelmans, Vaden Masrani, Frank Wood +2
The recently proposed Thermodynamic Variational Objective (TVO) leverages thermodynamic integration to provide a family of variational inference objectives, which both tighten and…
Semi-supervised Sequential Generative Models
Michael Teng, Tuan Anh Le, Adam Scibior +1
We introduce a novel objective for training deep generative time-series models with discrete latent variables for which supervision is only sparsely available. This instance of sem…
Enhancing Few-Shot Image Classification with Unlabelled Examples
Peyman Bateni, Jarred Barber, Jan-Willem van de Meent +1
We develop a transductive meta-learning method that uses unlabelled instances to improve few-shot image classification performance. Our approach combines a regularized Mahalanobis-…
Coping With Simulators That Don't Always Return
Andrew Warrington, Saeid Naderiparizi, Frank Wood
Deterministic models are approximations of reality that are easy to interpret and often easier to build than stochastic alternatives. Unfortunately, as nature is capricious, observ…
Planning as Inference in Epidemiological Models
Frank Wood, Andrew Warrington, Saeid Naderiparizi +8
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models. The kind of i…
Improved Few-Shot Visual Classification
Peyman Bateni, Raghav Goyal, Vaden Masrani +2
Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data. Most few-shot learning approaches to date…
Attention for Inference Compilation
William Harvey, Andreas Munk, Atılım Güneş Baydin +2
We present a new approach to automatic amortized inference in universal probabilistic programs which improves performance compared to current methods. Our approach is a variation o…
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…
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji +14
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remai…
The Thermodynamic Variational Objective
Vaden Masrani, Tuan Anh Le, Frank Wood
We introduce the thermodynamic variational objective (TVO) for learning in both continuous and discrete deep generative models. The TVO arises from a key connection between variati…
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
Yuan Zhou, Bradley J. Gram-Hansen, Tobias Kohn +3
We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variable…
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji +12
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which all…
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Bradley Gram-Hansen, Yuan Zhou, Tobias Kohn +3
Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs).…
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
Mario Lezcano Casado, Atilim Gunes Baydin, David Martinez Rubio +8
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from po…
Updating the VESICLE-CNN Synapse Detector
Andrew Warrington, Frank Wood
We present an updated version of the VESICLE-CNN algorithm presented by Roncal et al. (2014). The original implementation makes use of a patch-based approach. This methodology is k…
Bayesian Optimization for Probabilistic Programs
Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent +2
We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables. By using a series of code transformations, the evide…