Stefan Webb

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I am a final year DPhil student in the AIMS centre for doctoral training at Oxford supervised by Prof. M. Pawan Kumar and Prof. Yee Whye Teh. I am interested in advancing the state-of-the-art in Deep Generative Models, and developing new architectures and applications for them. During my DPhil, I’ve worked on distributed Bayesian learning, improving the design of inference networks, an importance component of amortized inference, as well as developing a new framework for neural network verification (under review).

Publications

2018

• 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.
  
  BibTeX

  arXiv

  @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.
  
  Abstract BibTeX

  arXiv

  PDF

  URL

  Software

  Project: sgmcmc

  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.
  
  Abstract BibTeX

  arXiv

  PDF

  Project: sgmcmc

  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}
  }
  

Software

2016

• L. Hasenclever , S. Webb , T. Lienart , S. Vollmer , B. Lakshminarayanan , C. Blundell , Y. W. Teh , Posterior Server. 2016.
  
  Abstract BibTeX

  URL

  Project: sgmcmc

  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}
  }