140 citations · 735 across the 46 of their papers we have counts for
13 papers · 1 filter
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…
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…
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
N. Siddharth, Brooks Paige, Jan-Willem van de Meent +5
Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the dat…
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 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…
A New Approach to Probabilistic Programming Inference
Frank Wood, Jan Willem van de Meent, Vikash Mansinghka
We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo. Our approach is simple to imp…
Black-Box Policy Search with Probabilistic Programs
Jan-Willem van de Meent, Brooks Paige, David Tolpin +1
In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box s…
Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka +1
Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference. A drawback of these techniques is that they rely on im…
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
Jan-Willem van de Meent, Jonathan E. Bronson, Frank Wood +2
We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterog…
Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures
Willie Neiswanger, Frank Wood
This paper proposes a technique for the unsupervised detection and tracking of arbitrary objects in videos. It is intended to reduce the need for detection and localization methods…
Inference in Hidden Markov Models with Explicit State Duration Distributions
Michael Dewar, Chris Wiggins, Frank Wood
In this letter we borrow from the inference techniques developed for unbounded state-cardinality (nonparametric) variants of the HMM and use them to develop a tuning-parameter free…