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  1. Department of Statistics
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  4. Publications

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).
Salehi S, others . Clonal fitness inferred from time-series modelling of single-cell cancer genomes. Nature. 2021; (Accepted).

2020

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.
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).

2019

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.
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.
al. KRCampbell. clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers. Genome Biology. 2019; (Accepted).
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.
Campbell K, al. et. clonealign: statistical integration of independent single-cell RNA and DNA-seq from human cancers. Genome Biology. 2019; 20: 1–12.

2018

Campbell K, Bouchard-Côté A, Shah S. Probabilistic inference of clonal gene expression through integration of RNA & DNA-seq at single-cell resolution. In HitSeq. 2018.
Zhang A, others . Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer. Cell. 2018; (Accepted).
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

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.
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.
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. arXiv. 2017; 1708.07592.
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.

2016

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.
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.
McPherson A, others . Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nature Genetics. 2016; 48: 758–767.
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.

2015

Roth A, McPherson A, Bouchard-Côté A, Shah S. Inference of clonal genotypes from single cell sequencing data. In HitSeq. 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.

2014

Shahriari B, Wang Z, Hoffman MW, Bouchard-Côté A, de Freitas N. An Entropy Search Portfolio for Bayesian Optimization. arXiv. 2014; 1406.4625.
Hajiaghayi M, Kirkpatrick B, Wang L, Bouchard-Côté A. Efficient continuous-time Markov chain estimation. In International Conference on Machine Learning (ICML). 2014. pp. 638–646.

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