Arnaud Doucet

Arnaud Doucet

Computational Statistics, Monte Carlo methods

Publications

2017

  • A. Doucet, C. Holmes, R. Bardenet, On Markov chain Monte Carlo Methods for Tall Data, 2017.
  • A. Bouchard-Côté, A. Doucet, A. Roth, Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models, Journal of Machine Learning Research, vol. 18, no. 28, 1–39, Apr. 2017.
  • C. J. Maddison, D. Lawson, G. Tucker, N. Heess, M. Norouzi, A. Mnih, A. Doucet, Y. W. Teh, Particle Value Functions, in ICLR 2017 Workshop Proceedings, 2017.
  • C. J. Maddison, D. Lawson, G. Tucker, N. Heess, M. Norouzi, A. Mnih, A. Doucet, Y. W. Teh, Filtering Variational Objectives, 2017.

2016

  • F. Caron, W. Neiswanger, F. Wood, A. Doucet, M. Davy, Generalized Pólya Urn for Time-Varying Pitman-Yor Processes, Journal of Machine Learning Research (JMLR), 2016.

2015

  • R. Bardenet, A. Doucet, C. C. Holmes, On Markov chain Monte Carlo methods for tall data, arXiv preprint arXiv:1505.02827, 2015.
  • R. Bardenet, A. Doucet, C. Holmes, Markov chain Monte Carlo and tall data, preprint, 2015.
  • T. Lienart, Y. W. Teh, A. Doucet, Expectation Particle Belief Propagation, in Advances in Neural Information Processing Systems (NIPS), 2015.
    Project: sgmcmc

2014

  • R. Bardenet, A. Doucet, C. C. Holmes, Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach, in Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014, 405–413.
  • R. Bardenet, A. Doucet, C. C. Holmes, An adaptive subsampling approach for MCMC inference in large datasets, in Proceedings of The 31st International Conference on Machine Learning, 2014, 405–413.
  • B. Paige, F. Wood, A. Doucet, Y. W. Teh, Asynchronous Anytime Sequential Monte Carlo, in Advances in Neural Information Processing Systems (NIPS), 2014.
    Project: sgmcmc

2012

  • A. Lee, F. Caron, A. Doucet, C. C. Holmes, . others, Bayesian sparsity-path-analysis of genetic association signal using generalized t priors, Statistical applications in genetics and molecular biology, vol. 11, no. 2, 1–29, 2012.

2010

  • A. Lee, F. Caron, A. Doucet, C. C. Holmes, A hierarchical Bayesian framework for constructing sparsity-inducing priors, arXiv preprint arXiv:1009.1914, 2010.
  • A. Lee, C. Yau, M. B. Giles, A. Doucet, C. C. Holmes, On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods, Journal of Computational and Graphical Statistics, vol. 19, no. 4, 769–789, 2010.

2009

  • F. Caron, A. Doucet, Bayesian Nonparametric Models on Decomposable Graphs, in Advances in Neural Information Processing Systems (NIPS), 2009.
  • S. Anjum, A. Doucet, C. C. Holmes, A boosting approach to structure learning of graphs with and without prior knowledge, Bioinformatics, vol. 25, no. 22, 2929–2936, 2009.

2008

  • F. Caron, M. Davy, A. Doucet, E. Duflos, P. Vanheeghe, Bayesian inference for linear dynamic models with Dirichlet process mixtures, IEEE Transactions on Signal Processing, vol. 56, no. 1, 71–84, 2008.
  • A. Jasra, A. Doucet, D. A. Stephens, C. C. Holmes, Interacting sequential Monte Carlo samplers for trans-dimensional simulation, Computational Statistics & Data Analysis, vol. 52, no. 4, 1765–1791, 2008.

2007

  • F. Caron, M. Davy, A. Doucet, Generalized Polya urn for time-varying Dirichlet process mixtures, in Uncertainty in Artificial Intelligence (UAI), 2007.