Frank Donald Wood

Associate Professor

Research Classification

Research Interests

Artificial Intelligence
Computational neuroscience
Machine Learning
Natural Language Processing
New models and inference algorithms
Probabilistic models
Probabilistic programming
Probabilistic programming systems
Reinforcement learning
Robotics
vision

Relevant Degree Programs

 
 

Open Research Positions

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Graduate Student Supervision

Master's Student Supervision (2010 - 2020)
On amortized inference in large-scale simulators (2020)

Motivated by the problem of amortized inference in large-scale simulators, we introduce a probabilistic programming library that brings us closer to this goal. This library enables us to perform Bayesian inference on any simulator written in a wide variety of programming languages, with minimal modification to the simulator's source code. However, there are challenges in achieving this goal in its most general meaning. In particular, we address the obstacles caused by unbounded loops. Existing approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. An instance of this is importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. We develop a new and efficient amortized importance sampling estimator. We prove finite variance of our estimator and empirically demonstrate our method's correctness and efficiency compared to existing alternatives on generative programs containing rejection sampling loops and discuss how to implement our method in a generic probabilistic programming framework.

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Membership Status

Member of G+PS
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Program Affiliations

 

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