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Publications by Alexandre Bouchard-Côté

2021

Bouchard-Côté A, Chern K, Cubranic D, Hosseini S, Hume J, Lepur M, et al.. Blang: Probabilitistic Programming for Combinatorial Spaces. Journal of Statistical Software. 2021; (Accepted).
Syed S, Romaniello V, Campbell T, Bouchard-Côté A. Parallel Tempering on Optimized Paths. In International Conference on Machine Learning (ICML). 2021.
Zhao T, Bouchard-Côté A. Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler. Journal of Machine Learning Research. 2021; 22: 1–41.
Syed S, Bouchard-Côté A, Deligiannidis G, Doucet A. Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme. Journal of Royal Statistical Society, Series B. 2021; (Accepted).

2020

Deligiannidis G, Paulin D, Bouchard-Côté A, Doucet A. Randomized Hamiltonian Monte Carlo as Scaling Limit of the Bouncy Particle Sampler and Dimension-Free Convergence Rates. Annals of Applied Probability. 2020; (Accepted).
Zhu P, Bouchard-Côté A, Campbell T. Slice Sampling for General Completely Random Measures. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). 2020. pp. 699–708.

2019

Deligiannidis G, Bouchard-Côté A, Doucet A. Exponential Ergodicity of the Bouncy Particle Sampler. Annals of Statistics. 2019; 47: 1268–1287.
Wang L, Wang S, Bouchard-Côté A. An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics. Systematic Biology. 2019; (Accepted).
Luo H, Freue GVCohen, Zhao X, Bouchard-Côté A, Burstyn I, Gustafson P. A new perspective on the benefits of the gene-environment independence in case-control studies. The Canadian Journal of Statistics. 2019; 47: 473–486.
Wang L, Wang S, Bouchard-Côté A. An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics. Systematic Biology. 2019; 69: 155–183.
Jun S-H, Wong SWK, Zidek JV, Bouchard-Côté A. Sequential decision model for inference and prediction on non-uniform hypergraphs with application to knot matching from computational forestry. Annals of Applied Statistics. 2019; 13: 1678–1707.
Cornish R, Vanetti P, Bouchard-Côté A, Deligiannidis G, Doucet A. Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. In International Conference on Machine Learning (ICML). 2019. pp. 1351–1360.
Zolaktaf S, Dannenberg F, Winfree E, Bouchard-Côté A, Schmidt M, Condon A. Efficient Parameter Estimation for DNA Kinetics Modeled as Continuous-Time Markov Chains. In The 25th International Conference on DNA Computing and Molecular Programming. 2019. pp. 80–99.

2018

Bierkens J, Bouchard-Côté A, Doucet A, Duncan AB, Fearnhead P, Lienart T, et al.. Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains. Statistics and Probability Letters. 2018; 136: 148–154.
Bouchard-Côté A, Vollmer SJ, Doucet A. The Bouncy Particle Sampler: A non-reversible rejection-free Markov chain Monte Carlo method. Journal of the American Statistical Association. 2018; 113: 855–867.
Zhang A, others . Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer. Cell. 2018; 173: 1755–1769.
Dorri F, Jewell S, Bouchard-Côté A, Shah S. MuClone: somatic mutation detection and classification through probabilistic integration of clonal population information. Communications Biology . 2018; 2.
Dorri F, Jewell S, Bouchard-Côté A, Shah S. MuClone: somatic mutation detection and classification through probabilistic integration of clonal population information. Communications Biology. 2018; 2: 1–10.

2017

Vanetti P, Bouchard-Côté A, Deligiannidis G, Doucet A. Piecewise Deterministic Markov Chain Monte Carlo. arXiv. 2017; 1707.05296.
Deligiannidis G, Bouchard-Côté A, Doucet A. Exponential ergodicity of the Bouncy Particle Sampler. arXiv. 2017; 1705.04579.
Bouchard-Côté A, Doucet A, Roth A. Particle Gibbs split-merge sampling for Bayesian inference in mixture models. Journal of Machine Learning Research. 2017; 18: 1–39.
Zhai Y, Bouchard-Côté A. A Poissonian model of indel rate variation for phylogenetic tree inference. Systematic Biology. 2017; 66: 698–714.
Jun S-H, Wong SWK, Zidek JV, Bouchard-Côté A. Sequential Graph Matching with Sequential Monte Carlo. In AISTATS. 2017. pp. 1075–1084.
Lindsten F, Johansen AM, Naesseth CA, Kirkpatrick B, Schon TB, Aston J, et al. Divide-and-conquer with sequential Monte Carlo. Journal of Computational Statistics and Graphics. 2017; 26: 445–458.
Lindsten F, Johansen AM, Naesseth CA, Kirkpatrick B, Schon TB, Aston J, et al. Divide-and-conquer with sequential Monte Carlo. Journal of Computational Statistics and Graphics. 2017; 26: 445–458.
Zhai Y, Bouchard-Côté A. A Poissonian model of indel rate variation for phylogenetic tree inference. Systematic Biology. 2017; (Accepted).
Bouchard-Côté A, Vollmer SJ, Doucet A. The Bouncy Particle Sampler: A non-reversible rejection-free Markov chain Monte Carlo method. Journal of the American Statistical Association. 2017; (Accepted).
McPherson A, Roth A, Ha G, Chauve C, Steif A, de Souza CPE, et al. ReMixT: clone-specific genomic structure estimation in cancer. Genome Biology. 2017; 18.
Salehi S, Steif A, Roth A, Aparicio S, Bouchard-Côté A, Shah SP. ddClone: joint statistical inference of clonal populations from single-cell and bulk tumor sequencing data. Genome Biology. 2017; 18.

2016

Zhai Y, Bouchard-Côté A. Inferring history of human populations using single-nucleotide polymorphism. Annals of Applied Statistics. 2016; 10: 2047–2074.
Shahriari B, Bouchard-Côté A, de Freitas N. Unbounded Bayesian optimization via regularization. In AISTATS. 2016. pp. 1168–1176.
Roth A, McPherson A, Laks E, Biele J, Yap D, Wan A, et al. Clonal genotype and population structure inference from single-cell tumor sequencing. Nature Methods. 2016; 13: 575–576.
Zhai Y, Bouchard-Côté A. Inferring history of human populations using single-nucleotide polymorphism. Annals of Applied Stat. 2016; 10: 2047–2074.
Bierkens J, Bouchard-Côté A, Doucet A, Duncan AB, Fearnhead P, Roberts G, et al.. Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains. arXiv. 2016; 1701.04244.

2015

Bouchard-Côté A, Doucet A, Roth A. Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models. arXiv. 2015; 1508.02663.
Jewell S, Spencer N, Bouchard-Côté A. Atomic spatial processes. In International Conference on Machine Learning (ICML). 2015. pp. 248–256.
Bouchard-Côté A, Doucet A, Roth A. Particle Gibbs split-merge sampling for Bayesian inference in mixture models. Journal of Machine Learning Research. 2015; (Accepted).
Zhao T, Cumberworth A, Wang Z, Gsponer J, de Freitas N, Bouchard-Côté A. Bayesian analysis of continuous time Markov chains with application to phylogenetic modelling. Bayesian Analysis. 2015; 11: 1203–1237.
Wang L, Bouchard-Côté A, Doucet A. Bayesian phylogenetic inference using the combinatorial sequential Monte Carlo method. Journal of the American Statistical Association. 2015; 110: 1362–1374.
McPherson A, Roth A, McAlpine J, Bouchard-Côté A, Shah SP. The Importance of Mutation Loss in Modelling Evolution and Metastasis in Genomically Unstable Cancers. In HitSeq. 2015.
Roth A, McPherson A, Bouchard-Côté A, Shah S. Inference of clonal genotypes from single cell sequencing data. In HitSeq. 2015.

2014

Roth A, Khattra J, Yap D, Wan A, Laks E, Biele J, et al. PyClone: statistical inference of clonal population structure in cancer. Nature Methods. 2014; 11: 396–398.
Shahriari B, Wang Z, Hoffman MW, Bouchard-Côté A, de Freitas N. An Entropy Search Portfolio for Bayesian Optimization. arXiv. 2014; 1406.4625.

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