Probabilistic inference, optimal experiment design, deep learning, probabilistic programming

I am a DPhil student in Statistics at the University of Oxford, supervised by Yee Whye Teh. I got my Bachelor’s and Master’s degrees in mathematics from Cambridge and worked as a machine learning engineer before joining the department. I am interested in statistical machine learning, with a current focus on optimal experiment design and mutual information estimation. I contribute to the deep probabilistic programming language Pyro, and use it in my research. Other research interests of mine include Bayesian nonparametric models of discrete structures, deep generative models, and Bayesian optimisation.

@inproceedings{BloemReddy:etal:2018,
author = {Bloem-Reddy, Benjamin and Foster, Adam and Mathieu, Emile and Teh, Yee Whye},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
title = {Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks},
month = aug,
year = {2018}
}

A. Foster
,
M. Jankowiak
,
E. Bingham
,
Y. W. Teh
,
T. Rainforth
,
N. Goodman
,
Variational Optimal Experiment Design: Efficient Automation of Adaptive Experiments, NeurIPS Workshop on Bayesian Deep Learning, 2018.

Bayesian optimal experimental design (OED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, the applicability of OED is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) for different experimental designs. We introduce a class of fast EIG estimators that leverage amortised variational inference and show that they provide substantial empirical gains over previous approaches. We integrate our approach into a deep probabilistic programming framework, thus making OED accessible to practitioners at large.

@article{foster2018voed,
title = {{Variational Optimal Experiment Design: Efficient Automation of Adaptive Experiments}},
author = {Foster, Adam and Jankowiak, Martin and Bingham, Eli and Teh, Yee Whye and Rainforth, Tom and Goodman, Noah},
journal = {NeurIPS Workshop on Bayesian Deep Learning},
year = {2018}
}

2017

B. Bloem-Reddy
,
E. Mathieu
,
A. Foster
,
T. Rainforth
,
H. Ge
,
M. Lomelí
,
Z. Ghahramani
,
Y. W. Teh
,
Sampling and inference for discrete random probability measures in probabilistic programs, NIPS Workshop on Advances in Approximate Bayesian Inference, 2017.

We consider the problem of sampling a sequence from a discrete random probability measure (RPM) with countable support, under (probabilistic) constraints of finite memory and computation. A canonical example is sampling from the Dirichlet Process, which can be accomplished using its stick-breaking representation and lazy initialization of its atoms. We show that efficiently lazy initialization is possible if and only if a size-biased representation of the discrete RPM is used. For models constructed from such discrete RPMs, we consider the implications for generic particle-based inference methods in probabilistic programming systems. To demonstrate, we implement SMC for Normalized Inverse Gaussian Process mixture models in Turing.

@article{bloemreddy2017rpm,
title = {Sampling and inference for discrete random probability measures in probabilistic programs},
author = {Bloem-Reddy, Benjamin and Mathieu, Emile and Foster, Adam and Rainforth, Tom and Ge, Hong and Lomelí, María and Ghahramani, Zoubin and Teh, Yee Whye},
journal = {NIPS Workshop on Advances in Approximate Bayesian Inference},
year = {2017}
}