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Thibaut Lienart

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Inference on graphical models, expectation propagation, particle methods

I’m currently a Postdoc at the University of Melbourne in adversarial ML with Prof. Ben Rubinstein. I completed my DPhil in 2018, co-supervised by Prof. Yee Whye Teh and Prof. Arnaud Doucet, my thesis was on inference methods for graphical models.

Publications

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 (JMLR), Oct. 2017.
  
  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{HasWebLie2017a,
    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},
    journal = {Journal of Machine Learning Research (JMLR)},
    month = oct,
    year = {2017},
    bdsk-url-1 = {https://arxiv.org/pdf/1512.09327.pdf}
  }
  

• 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.
  
  Project: bigbayes 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

• T. Lienart , Y. W. Teh , A. Doucet , Expectation Particle Belief Propagation, in Advances in Neural Information Processing Systems (NeurIPS), 2015.
  
  Project: sgmcmc

  We propose an original particle-based implementation of the Loopy Belief Propagation (LPB) algorithm for pairwise Markov Random Fields (MRF) on a continuous state space. The algorithm constructs adaptively efficient proposal distributions approximating the local beliefs at each note of the MRF. This is achieved by considering proposal distributions in the exponential family whose parameters are updated iterately in an Expectation Propagation (EP) framework. The proposed particle scheme provides consistent estimation of the LBP marginals as the number of particles increases. We demonstrate that it provides more accurate results than the Particle Belief Propagation (PBP) algorithm of Ihler and McAllester (2009) at a fraction of the computational cost and is additionally more robust empirically. The computational complexity of our algorithm at each iteration is quadratic in the number of particles. We also propose an accelerated implementation with sub-quadratic computational complexity which still provides consistent estimates of the loopy BP marginal distributions and performs almost as well as the original procedure.

  @inproceedings{LieTehDou2015a,
    author = {Lienart, T. and Teh, Y. W. and Doucet, A.},
    booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
    title = {Expectation Particle Belief Propagation},
    year = {2015},
    bdsk-url-1 = {http://papers.nips.cc/paper/5674-expectation-particle-belief-propagation},
    bdsk-url-2 = {http://papers.nips.cc/paper/5674-expectation-particle-belief-propagation.pdf},
    bdsk-url-3 = {http://papers.nips.cc/paper/5674-expectation-particle-belief-propagation-supplemental.zip}
  }
  

Software

2016

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