Computational Statistics, Monte Carlo methods

2019

• , , , 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.
• R. Cornish , A. L. Caterini , , , Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows, arXiv preprint arXiv:1909.13833, 2019.
• S. Syed , A. Bouchard-Côté , , , Non-Reversible Parallel Tempering: an Embarassingly Parallel MCMC Scheme, arXiv preprint arXiv:1905.02939, 2019.
• , , , Bernoulli Race Particle Filters, AISTATS, 2019.

2017

• , D. Lawson , G. Tucker , N. Heess , M. Norouzi , A. Mnih , , , Filtering Variational Objectives, in Advances in Neural Information Processing Systems (NeurIPS), 2017.
Project: deepmind
• A. Bouchard-Côté , , , 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 , , J. F. Giovannelli , , Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling, in Advances in Neural Information Processing Systems (NeurIPS), 2017.
• , W. Neiswanger , F. Wood , , M. Davy , Generalized Pólya Urn for Time-Varying Pitman-Yor Processes, Journal of Machine Learning Research (JMLR), vol. 18, no. 27, 1–32, 2017.
• , A. Bouchard-Côté , , Exponential Ergodicity of the Bouncy Particle Sampler, to appear in Annals of Statistics arXiv:1705.04579, 2017.
• P. Vanetti , A. Bouchard-Côté , , , Piecewise Deterministic Markov Chain Monte Carlo, arXiv preprint arXiv:1707.05296, 2017.
• J. Heng , A. N. Bishop , , , Controlled Sequential Monte Carlo, arXiv preprint arXiv:1708.08396, 2017.
• , C. Holmes , , On Markov chain Monte Carlo Methods for Tall Data, 2017.
• , D. Lawson , G. Tucker , N. Heess , M. Norouzi , A. Mnih , , , Particle Value Functions, in ICLR 2017 Workshop Proceedings, 2017.
Project: deepmind

2016

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

2014

• , , , 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.
• , , , 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 , , , Asynchronous Anytime Sequential Monte Carlo, in Advances in Neural Information Processing Systems (NeurIPS), 2014.
Project: sgmcmc

2012

• A. Lee , , , , . 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 , , , , A hierarchical Bayesian framework for constructing sparsity-inducing priors, arXiv preprint arXiv:1009.1914, 2010.
• A. Lee , C. Yau , M. B. Giles , , , 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

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

2008

• , M. Davy , , 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 , , D. A. Stephens , , Interacting sequential Monte Carlo samplers for trans-dimensional simulation, Computational Statistics & Data Analysis, vol. 52, no. 4, 1765–1791, 2008.

2007

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