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Publications by Alexandre Bouchard-Côté

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).
al. KRCampbell. clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers. Genome Biology. 2019;(Accepted).

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.
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.

2017

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.
Vanetti P, Bouchard-Côté A, Deligiannidis G, Doucet A. Piecewise Deterministic Markov Chain Monte Carlo. arXiv. 2017;1707.05296.
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.
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).

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.
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

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.
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.
Bouchard-Côté A. Sequential Monte Carlo (SMC) for Bayesian phylogenetics. In: Chen M-H, Kuo L, Lewis PO (eds.). Bayesian phylogenetics: methods, algorithms, and applications. 2014. pp. 163–186.
Jun S-H, Bouchard-Côté A. Memory (and time) efficient sequential Monte Carlo. In International Conference on Machine Learning (ICML). 2014. pp. 514–522.
Lindsten F, Johansen AM, Naesseth CA, Kirkpatrick B, Schon TB, Aston J, et al. Divide-and-Conquer with Sequential Monte Carlo. arXiv. 2014;1406.4993.

2013

Bouchard-Côté A, Jordan MI. Evolutionary inference via the Poisson indel process. Proceedings of the National Academy of Sciences. 2013;110:1160–1166.
Bouchard-Côté A, Hall D, Griffiths TL, Klein D. Automated reconstruction of ancient languages using probabilistic models of sound change. Proceedings of the National Academy of Sciences. 2013;110:4224–4229.
Bouchard-Côté A. A note on probabilistic models over strings: the linear algebra approach. Bulletin of Mathematical Biology. 2013;75:2529–2550.
Bouchard-Côté A. A Note on Probabilistic Models over Strings: Inference and  Representation with Indexed Matrices. arXiv. 2013;1301.5054.
Hajiaghayi M, Kirkpatrick B, Wang L, Bouchard-Côté A. Efficient Continuous-Time Markov Chain Estimation. arXiv. 2013;1309.325.
Jun S-H, Bouchard-Côté A. A Stochastic Map View of Sequential Monte Carlo with Applications to Memory and Network Efficiency. In Randomized Algorithm Workshop at Advances in Neural Information Processing Systems 26 (NIPS). 2013.

2012

Bouchard-Côté A, Jordan MI. The Poisson Indel Process. arXiv. 2012;1207.6327.
Wang L, Bouchard-Côté A. Harnessing Non-Local Evolutionary Events for Tree Inference. In Society for Molecular Biology and Evolution. 2012.
Bouchard-Côté A, Sankararaman S, Jordan MI. Phylogenetic inference via sequential Monte Carlo. Systematic Biology. 2012;61:579–593.
Bouchard-Côté A, Kirkpatrick B. Bayesian pedigree analysis using measure factorization. In Advances in Neural Information Processing Systems 25 (NIPS). 2012. pp. 2906–2914.
Jun S-H, Wang L, Bouchard-Côté A. Entangled Monte Carlo. In Advances in Neural Information Processing Systems 25 (NIPS). 2012. pp. 2735–2743.

2011

Bouchard-Côté A, Zidek JV. Discussion: Bayesian priors for loss matching. International Statistical Review. 2011;80:83–86.
Saeedi A, Bouchard-Côté A. Priors over recurrent continuous time processes. In Advances in Neural Information Processing Systems 24 (NIPS). 2011. pp. 2052–2060.

2010

Bouchard-Côté A, Sankararaman S, Jordan MI. Bayesian Phylogenetic Inference using Sequential Monte Carlo Algorithms. In Society for Molecular Biology and Evolution. 2010.
Bouchard-Côté A. Probabilistic Models of Evolution and Language Change. UC Berkeley; 2010.

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