Arnaud Doucet

Arnaud Doucet

Computational Statistics, Monte Carlo methods

Publications

2021

  • J. Thornton , A. Corenflos , G. Deligiannidis , A. Doucet , Differentiable Particle Filtering, 2021.

2020

  • S. M. Schmon , G. Deligiannidis , A. Doucet , M. K. Pitt , Large-sample asymptotics of the pseudo-marginal method, Biometrika, Jul. 2020.
  • S. Schmon , A. Doucet , G. Deligiannidis , Bernoulli race particle filters, in AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, 2020.
  • L. Middleton , G. Deligiannidis , A. Doucet , P. Jacob , Unbiased markov chain monte carlo for intractable target distributions, Electronic Journal of Statistics, vol. 14, no. 2, 2842–2891, 2020.
  • J. Heng , A. Bishop , G. Deligiannidis , A. Doucet , Controlled sequential monte carlo, Annals of Statistics, vol. 48, no. 5, 2904–2929, 2020.
  • L. Middleton , G. Deligiannidis , A. Doucet , P. Jacob , Unbiased smoothing using particle independent metropolis-hastings, in AISTATS 2019 - 22nd International Conference on Artificial Intelligence and Statistics, 2020.
  • S. Schmon , G. Deligiannidis , A. Doucet , M. Pitt , Large sample asymptotics of the pseudo-marginal method, Biometrika, 2020.
  • G. Deligiannidis , A. Doucet , S. Rubenthaler , Ensemble Rejection Sampling, aarXiv:2001.0988, 2020.
  • R. Cornish , A. Caterini , G. Deligiannidis , A. Doucet , Relaxing bijectivity constraints with continuously indexed normalising flows, in ICML, 2020, 2133–2143.
  • S. Hayou , E. Clerico , B. He , G. Deligiannidis , A. Doucet , J. Rousseau , Stable ResNet, AISTATS 2021, 2020.

2019

  • E. Dupont , A. Doucet , Y. W. Teh , Augmented Neural ODEs, in Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d’ Alché-Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, 3134–3144.
  • G. Deligiannidis , A. Bouchard-Côté , A. Doucet , Exponential ergodicity of the bouncy particle sampler, Annals of Statistics, vol. 47, no. 3, 1268–1287, 2019.
  • R. Cornish , P. Vanetti , A. Bouchard-Côté , G. Deligiannidis , A. Doucet , Scalable metropolis-hastings for exact Bayesian inference with large datasets, in 36th International Conference on Machine Learning, ICML 2019, 2019, vol. 2019-June, 2398–2429.
  • S. Syed , A. Bouchard-Côté , G. Deligiannidis , A. Doucet , Non-reversible parallel tempering: a scalable highly parallel MCMC scheme, arXiv:1905.02939, 2019.
  • S. M. Schmon , G. Deligiannidis , A. Doucet , Bernoulli Race Particle Filters, AISTATS, 2019.

2018

  • A. Caterini , A. Doucet , D. Sejdinovic , Hamiltonian Variational Auto-Encoder, in Advances in Neural Information Processing Systems (NeurIPS), 2018, to appear.
  • G. Deligiannidis , A. Doucet , M. Pitt , The correlated pseudomarginal method, JRSSB, vol. 80, no. 5, 839–870, 2018.
  • G. Deligiannidis , D. Paulin , A. Bouchard-Côté , A. Doucet , Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates, To appear in Annals of Applied Probability, 2018.

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 (NeurIPS), 2017.
    Project: deepmind
  • 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.
  • A. Barbos , F. Caron , J. F. Giovannelli , A. Doucet , Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling, in Advances in Neural Information Processing Systems (NeurIPS), 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.
  • P. Vanetti , A. Bouchard-Côté , G. Deligiannidis , A. Doucet , Piecewise-Deterministic Markov Chain Monte Carlo, arXiv preprint arXiv:1707.05296, 2017.
  • A. Doucet , C. Holmes , R. Bardenet , On Markov chain Monte Carlo Methods for Tall Data, 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.
    Project: deepmind

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

  • 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 (NeurIPS), 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 (NeurIPS), 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 (NeurIPS), 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.