Arnaud Doucet

Arnaud Doucet

Computational Statistics, Monte Carlo methods

Publications

2017

  • A. Barbos , F. Caron , J. F. Giovannelli , A. Doucet , Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling, in Advances in Neural Information Processing Systems (NIPS), 2017.
  • 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), vol. 18, no. 27, 1–32, 2017.
  • G. Deligiannidis , A. Bouchard-Côté , A. Doucet , Exponential Ergodicity of the Bouncy Particle Sampler, 2017.
  • P. Vanetti , A. Bouchard-Côté , G. Deligiannidis , A. Doucet , Piecewise Deterministic Markov Chain Monte Carlo, 2017.
  • J. Heng , A. N. Bishop , G. Deligiannidis , A. Doucet , Controlled Sequential Monte Carlo, 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, in Advances in Neural Information Processing Systems (NIPS), 2017.

2016

  • T. Rainforth , C. A. Naesseth , F. Lindsten , B. Paige , J. Meent , A. Doucet , F. Wood , Interacting Particle Markov Chain Monte Carlo, in Proceedings of the 33rd International Conference on Machine Learning, 2016, vol. 48.

2015

  • G. Deligiannidis , A. Doucet , M. K. Pitt , The correlated pseudo-marginal method, 2015.
  • A. Doucet , M. Pitt , G. Deligiannidis , R. Kohn , Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator, Biometrika, vol. 102, no. 2, 295–313, 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.