S. Webb
,
A. Golinski
,
R. Zinkov
,
N. Siddharth
,
T. Rainforth
,
Y. W. Teh
,
F. Wood
,
Faithful Inversion of Generative Models for Effective Amortized Inference, in Advances in Neural Information Processing Systems (NIPS), 2018.

@inproceedings{Webb2018,
author = {Webb, Stefan and Golinski, Adam and Zinkov, Robert and Siddharth, N. and Rainforth, Tom and Teh, Yee Whye and Wood, Frank},
title = {Faithful Inversion of Generative Models for Effective Amortized Inference},
booktitle = {Advances in Neural Information Processing Systems (NIPS)},
year = {2018}
}

2017

L. Hasenclever
,
S. Webb
,
T. Lienart
,
S. Vollmer
,
B. Lakshminarayanan
,
C. Blundell
,
Y. W. Teh
,
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server, Journal of Machine Learning Research, vol. 18, no. 106, 1–37, 2017.

This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a dataset is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks.

@article{HasWebLie2015a,
author = {Hasenclever, Leonard and Webb, Stefan and Lienart, Thibaut and Vollmer, Sebastian and Lakshminarayanan, Balaji and Blundell, Charles and Teh, Yee Whye},
title = {Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server},
journal = {Journal of Machine Learning Research},
year = {2017},
volume = {18},
number = {106},
pages = {1-37}
}

2015

L. Hasenclever
,
S. Webb
,
T. Lienart
,
S. Vollmer
,
B. Lakshminarayanan
,
C. Blundell
,
Y. W. Teh
,
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server, 2015.

This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a dataset is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks.

@unpublished{HasWebLie2015b,
author = {Hasenclever, L. and Webb, S. and Lienart, T. and Vollmer, S. and Lakshminarayanan, B. and Blundell, C. and Teh, Y. W.},
note = {ArXiv e-prints: 1512.09327},
title = {Distributed {B}ayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server},
year = {2015},
bdsk-url-1 = {https://arxiv.org/pdf/1512.09327.pdf}
}

This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a dataset is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks.

@software{HasWebLie2016a,
author = {Hasenclever, L. and Webb, S. and Lienart, T. and Vollmer, S. and Lakshminarayanan, B. and Blundell, C. and Teh, Y. W.},
title = {Posterior Server},
year = {2016},
bdsk-url-1 = {https://github.com/BigBayes/PosteriorServer}
}