OxCSML : People : François Caron

Statistical Machine Learning, Bayesian methods, Bayesian nonparametrics, Statistical Network Analysis

I am an Associate Professor in Statistics at the University of Oxford, a Tutorial Fellow of Keble College and a Faculty Fellow of the Alan Turing Institute.

Publications

2019

J. Lee , L. James , S. Choi , F. Caron , A Bayesian model for sparse graphs with flexible degree distributionand overlapping community structure, in Artificial Intelligence and Statistics (AISTATS), 2019.

Project: bigbayes

@inproceedings{Lee2019,
  author = {Lee, Juho and James, Lancelot and Choi, Seungjin and Caron, Fran\c cois},
  title = {A Bayesian model for sparse graphs with flexible degree distributionand overlapping community structure},
  booktitle = {Artificial Intelligence and Statistics (AISTATS)},
  note = {ArXiv e-prints: 1810.01778},
  year = {2019},
  month = apr
}

F. Ayed , F. Caron , Nonnegative Bayesian nonparametric factor models with completely random measures for community detection, 2019.

@unpublished{Ayed:Caron,
  author = {Ayed, F. and Caron, F.},
  title = {Nonnegative Bayesian nonparametric factor models with completely random measures for community detection},
  year = {2019}
}

F. Ayed , J. Lee , F. Caron , Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior, 2019.

@unpublished{Ayed:Lee:Caron,
  author = {Ayed, F. and Lee, J. and Caron, F.},
  title = {Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with Double Power-law Behavior},
  year = {2019}
}

C. Naik , F. Caron , J. Rousseau , Sparse Networks with Core-Periphery Structure, 2019.

@unpublished{naik2019sparse,
  author = {Naik, Cian and Caron, Fran{\c{c}}ois and Rousseau, Judith},
  title = {Sparse Networks with Core-Periphery Structure},
  year = {2019}
}

2018

X. Miscouridou , F. Caron , Y. W. Teh , Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data, in Advances in Neural Information Processing Systems (NeurIPS), 2018.

Project: bigbayes

@inproceedings{HawkesInteractions,
  author = {Miscouridou, Xenia and Caron, Fran\c ois and Teh, Yee Whye},
  title = {Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  note = {ArXiv e-prints: 1803.06070},
  year = {2018},
  month = dec
}

2017

G. Di Benedetto , F. Caron , Y. W. Teh , Non-exchangeable random partition models for microclustering, Nov-2017.

Project: bigbayes

@unpublished{DiBenedetto2017,
  author = {Di Benedetto, Giuseppe and Caron, Fran{\c{c}}ois and Teh, Yee Whye},
  title = {Non-exchangeable random partition models for microclustering},
  note = {ArXiv e-prints:1711.07287},
  archiveprefix = {arXiv},
  year = {2017},
  month = nov
}

A. Todeschini , X. Miscouridou , F. Caron , Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities, Aug-2017.

Project: bigbayes

@unpublished{OverlappingCommunityGraph,
  author = {Todeschini, Adrien and Miscouridou, Xenia and Caron, Fran\c ois},
  title = {Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities},
  note = {ArXiv e-prints: 1602.02114},
  archiveprefix = {arXiv},
  year = {2017},
  month = aug
}

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.

@incollection{Barbos2017,
  title = {Clone {MCMC}: Parallel High-Dimensional {G}aussian {G}ibbs Sampling},
  author = {Barbos, A. and Caron, F. and Giovannelli, J. F. and Doucet, A.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017}
}

F. Caron , E. B. Fox , Sparse Graphs using exchangeable random measures, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 79, no. 5, 1295–1366, 2017.

@article{Caron2017,
  title = {Sparse Graphs using exchangeable random measures},
  author = {Caron, F. and Fox, E. B.},
  journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
  year = {2017},
  number = {5},
  pages = {1295-1366},
  volume = {79}
}

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.

@article{Caron2017a,
  title = {Generalized {P}{\'o}lya Urn for Time-Varying Pitman-Yor Processes},
  author = {Caron, F. and Neiswanger, W. and Wood, F. and Doucet, A. and Davy, M.},
  journal = {Journal of Machine Learning Research (JMLR)},
  year = {2017},
  number = {27},
  pages = {1-32},
  volume = {18}
}

E. Matechou , F. Caron , Modelling individual migration patterns using a Bayesian nonparametric approach for capture-recapture data, Annals of Applied Statistics, vol. 11, no. 1, 21–40, 2017.

@article{Matechou2017,
  title = {Modelling individual migration patterns using a Bayesian nonparametric approach for capture-recapture data},
  author = {Matechou, E. and Caron, F.},
  journal = {Annals of Applied Statistics},
  year = {2017},
  number = {1},
  pages = {21-40},
  volume = {11}
}

F. Caron , J. Rousseau , On sparsity and power-law properties of graphs based on exchangeable point processes, arXiv preprint arXiv:1708.03120, 2017.

@article{caron2017sparsity,
  title = {On sparsity and power-law properties of graphs based on exchangeable point processes},
  author = {Caron, Fran{\c{c}}ois and Rousseau, Judith},
  journal = {arXiv preprint arXiv:1708.03120},
  year = {2017}
}

2016

K. Palla , F. Caron , Y. W. Teh , A Bayesian nonparametric model for sparse dynamic networks, Jun-2016.
Project: bigbayes

We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modeling the sociability of that node. Sociabilities are assumed to evolve over time, and are modeled via a dynamic point process model. The model is able to (a) capture smooth evolution of the interaction between nodes, allowing edges to appear/disappear over time (b) capture long term evolution of the sociabilities of the nodes (c) and yield sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying gamma process. We provide some theoretical insights into the model and apply it to three real world datasets.
```
@unpublished{PallaCaronTeh2016,
  author = {Palla, Konstantina and Caron, Francois and Teh, Yee Whye},
  title = {A Bayesian nonparametric model for sparse dynamic networks},
  note = {ArXiv e-prints: 1607.01624},
  archiveprefix = {arXiv},
  year = {2016},
  month = jun
}
```

T. Gray-Davies , C. Holmes , F. Caron , Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks, Electronic Journal of Statistics, vol. 10, 1807–1828, 2016.

@article{Gray-Davies2016,
  title = {Scalable Bayesian nonparametric regression via a {Plackett-Luce} model for conditional ranks},
  author = {Gray-Davies, T. and Holmes, C. and Caron, F.},
  journal = {Electronic Journal of Statistics},
  year = {2016},
  pages = {1807-1828},
  volume = {10}
}

T. Gray-Davies , C. C. Holmes , F. Caron , . others , Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks, Electronic Journal of Statistics, vol. 10, no. 2, 1807–1828, 2016.

@article{gray2016scalable,
  title = {Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks},
  author = {Gray-Davies, Tristan and Holmes, Chris C and Caron, Fran{\c{c}}ois and others},
  journal = {Electronic Journal of Statistics},
  volume = {10},
  number = {2},
  pages = {1807--1828},
  year = {2016},
  publisher = {The Institute of Mathematical Statistics and the Bernoulli Society}
}

K. Palla , F. Caron , Y. W. Teh , Bayesian Nonparametrics for Sparse Dynamic Networks, 2016.
Project: bigbayes

We propose a Bayesian nonparametric prior for time-varying networks. To each node of the network is associated a positive parameter, modeling the sociability of that node. Sociabilities are assumed to evolve over time, and are modeled via a dynamic point process model. The model is able to (a) capture smooth evolution of the interaction between nodes, allowing edges to appear/disappear over time (b) capture long term evolution of the sociabilities of the nodes (c) and yield sparse graphs, where the number of edges grows subquadratically with the number of nodes. The evolution of the sociabilities is described by a tractable time-varying gamma process. We provide some theoretical insights into the model and apply it to three real world datasets.
```
@unpublished{PalCarTeh2016a,
  author = {Palla, K. and Caron, F. and Teh, Y. W.},
  note = {ArXiv e-prints: 1607.01624},
  title = {Bayesian Nonparametrics for Sparse Dynamic Networks},
  year = {2016},
  bdsk-url-1 = {https://arxiv.org/pdf/1607.01624.pdf}
}
```

2015

C. C. Holmes , F. Caron , J. E. Griffin , D. A. Stephens , Two-sample Bayesian nonparametric hypothesis testing, Bayesian Analysis, vol. 10, no. 2, 297–320, 2015.

@article{Holmes2015,
  title = {Two-sample Bayesian nonparametric hypothesis testing},
  author = {Holmes, C. C. and Caron, F. and Griffin, J. E. and Stephens, D. A.},
  journal = {Bayesian Analysis},
  year = {2015},
  number = {2},
  pages = {297--320},
  volume = {10},
  file = {1422884976:https\://projecteuclid.org/download/pdfview_1/euclid.ba/1422884976:PDF},
  publisher = {International Society for Bayesian Analysis}
}

C. C. Holmes , F. Caron , J. E. Griffin , D. A. Stephens , . others , Two-sample Bayesian nonparametric hypothesis testing, Bayesian Analysis, vol. 10, no. 2, 297–320, 2015.

@article{holmes2015two,
  title = {Two-sample Bayesian nonparametric hypothesis testing},
  author = {Holmes, Chris C and Caron, Fran{\c{c}}ois and Griffin, Jim E and Stephens, David A and others},
  journal = {Bayesian Analysis},
  volume = {10},
  number = {2},
  pages = {297--320},
  year = {2015},
  publisher = {International Society for Bayesian Analysis}
}

2014

A. Todeschini , F. Caron , M. Fuentes , P. Legrand , P. Del Moral , Biips: software for Bayesian inference with interacting particle systems, arXiv:1412.3779, 2014.

@article{Todeschini2014,
  title = {Biips: software for Bayesian inference with interacting particle systems},
  author = {Todeschini, Adrien and Caron, Fran{\c{c}}ois and Fuentes, Marc and Legrand, Pierrick and Del Moral, Pierre},
  journal = {arXiv:1412.3779},
  year = {2014}
}

F. Caron , Y. W. Teh , B. T. Murphy , Bayesian Nonparametric Plackett-Luce Models for the Analysis of Preferences for College Degree Programmes, Annals of Applied Statistics, vol. 8, no. 2, 1145–1181, 2014.
In this paper we propose a Bayesian nonparametric model for clustering partial ranking data. We start by developing a Bayesian nonparametric extension of the popular Plackett–Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a completely random measure. We characterise the posterior distribution given data, and derive a simple and effective Gibbs sampler for posterior simulation. We then develop a Dirichlet process mixture extension of our model and apply it to investigate the clustering of preferences for college degree programmes amongst Irish secondary school graduates. The existence of clusters of applicants who have similar preferences for degree programmes is established and we determine that subject matter and geographical location of the third level institution characterise these clusters.
```
@article{CarTehMur2014a,
  author = {Caron, F. and Teh, Y. W. and Murphy, B. T.},
  doi = {10.1214/14-AOAS717},
  journal = {Annals of Applied Statistics},
  number = {2},
  pages = {1145-1181},
  title = {{B}ayesian Nonparametric {P}lackett-{L}uce Models for the Analysis of Preferences for College Degree Programmes},
  volume = {8},
  year = {2014},
  bdsk-url-1 = {https://projecteuclid.org/euclid.aoas/1404229529},
  bdsk-url-2 = {http://dx.doi.org/10.1214/14-AOAS717},
  bdsk-url-3 = {https://projecteuclid.org/download/pdfview_1/euclid.aoas/1404229529},
  bdsk-url-4 = {http://www.stats.ox.ac.uk/\\~{}caron/code/bnppl/index.html}
}
```

2013

A. Todeschini , F. Caron , M. Chavent , Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms, in Advances in Neural Information Processing Systems (NeurIPS), 2013, 845–853.

@inproceedings{Todeschini2013,
  title = {Probabilistic low-rank matrix completion with adaptive spectral regularization algorithms},
  author = {Todeschini, Adrien and Caron, Fran{\c{c}}ois and Chavent, Marie},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2013},
  pages = {845--853},
  file = {5005-probabilistic-low-rank-matrix-completion-with-adaptive-spectral-regularization-algorithms.pdf:http\://papers.nips.cc/paper/5005-probabilistic-low-rank-matrix-completion-with-adaptive-spectral-regularization-algorithms.pdf:PDF}
}

2012

C. Archambeau , F. Caron , Plackett-Luce regression: A new Bayesian model for polychotomous data, in Uncertainty in Artificial Intelligence (UAI), 2012.

@inproceedings{Archambeau2012,
  title = {{Plackett-Luce} regression: A new {B}ayesian model for polychotomous data},
  author = {Archambeau, C{\'e}dric and Caron, Francois},
  booktitle = {Uncertainty in Artificial Intelligence (UAI)},
  year = {2012},
  file = {Archambeau_Caron_UAI_2012.pdf:http\://www.stats.ox.ac.uk/\\~{}caron/Publications/Archambeau_Caron_UAI_2012.pdf:PDF}
}

F. Caron , Bayesian nonparametric models for bipartite graphs, in Advances in Neural Information Processing Systems (NeurIPS), 2012.

@inproceedings{Caron2012,
  title = {Bayesian nonparametric models for bipartite graphs},
  author = {Caron, F.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {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.

@article{lee2012bayesian,
  title = {Bayesian sparsity-path-analysis of genetic association signal using generalized t priors},
  author = {Lee, Anthony and Caron, Francois and Doucet, Arnaud and Holmes, Chris C. and others},
  journal = {Statistical applications in genetics and molecular biology},
  volume = {11},
  number = {2},
  pages = {1--29},
  year = {2012},
  publisher = {Walter de Gruyter GmbH \& Co. KG}
}

F. Caron , C. C. Holmes , E. Rio , On the sampling distribution of an $\backslash ell\^ 2$ distance between Empirical Distribution Functions with applications to nonparametric testing, PhD thesis, INRIA, 2012.

@phdthesis{caron2012sampling,
  title = {On the sampling distribution of an $$\backslash$ ell\^{} 2$ distance between Empirical Distribution Functions with applications to nonparametric testing},
  author = {Caron, Francois and Holmes, Chris C. and Rio, Emmanuel},
  year = {2012},
  school = {INRIA}
}

F. Caron , C. C. Holmes , E. Rio , On the sampling distribution of an l2 norm of the Empirical Distribution Function, with applications to two-sample nonparametric testing, 2012.

@article{caron2012samplinh,
  title = {On the sampling distribution of an l2 norm of the Empirical Distribution Function, with applications to two-sample nonparametric testing},
  author = {Caron, Francois and Holmes, Chris C. and Rio, Emmanuel},
  year = {2012}
}

F. Caron , Y. W. Teh , Bayesian Nonparametric Models for Ranked Data, in Advances in Neural Information Processing Systems (NeurIPS), 2012.
We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a gamma process. We derive a posterior characterization and a simple and effective Gibbs sampler for posterior simulation. We then develop a time-varying extension of our model, and apply our model to the New York Times lists of weekly bestselling books.
```
@inproceedings{CarTeh2012a,
  author = {Caron, F. and Teh, Y. W.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  title = {Bayesian Nonparametric Models for Ranked Data},
  year = {2012},
  bdsk-url-1 = {http://papers.nips.cc/paper/4624-bayesian-nonparametric-models-for-ranked-data},
  bdsk-url-2 = {http://papers.nips.cc/paper/4624-bayesian-nonparametric-models-for-ranked-data.pdf},
  bdsk-url-3 = {http://papers.nips.cc/paper/4624-bayesian-nonparametric-models-for-ranked-data-supplemental.zip}
}
```

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.

@article{lee2010hierarchical,
  title = {A hierarchical Bayesian framework for constructing sparsity-inducing priors},
  author = {Lee, Anthony and Caron, Francois and Doucet, Arnaud and Holmes, Chris C.},
  journal = {arXiv preprint arXiv:1009.1914},
  year = {2010}
}

2009

F. Caron , A. Doucet , Bayesian Nonparametric Models on Decomposable Graphs, in Advances in Neural Information Processing Systems (NeurIPS), 2009.

@inproceedings{Caron2009,
  title = {Bayesian Nonparametric Models on Decomposable Graphs},
  author = {Caron, F. and Doucet, A.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2009},
  owner = {caron},
  timestamp = {2016.10.24}
}

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.

@article{Caron2008,
  title = {Bayesian inference for linear dynamic models with {D}irichlet process mixtures},
  author = {Caron, F. and Davy, M. and Doucet, A. and Duflos, E. and Vanheeghe, P.},
  journal = {IEEE Transactions on Signal Processing},
  year = {2008},
  number = {1},
  pages = {71--84},
  volume = {56},
  owner = {caron},
  publisher = {IEEE},
  timestamp = {2016.10.24}
}

2007

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

@inproceedings{Caron2007,
  title = {{Generalized Polya urn for time-varying Dirichlet process mixtures}},
  author = {Caron, F. and Davy, M. and Doucet, A.},
  booktitle = {Uncertainty in Artificial Intelligence (UAI)},
  year = {2007},
  owner = {caron},
  timestamp = {2016.10.24}
}

Software

2017

A. Todeschini , X. Miscouridou , F. Caron , SNetOC. 2017.

Project: bigbayes

@software{SNetOC2017,
  author = {Todeschini, Adrien and Miscouridou, Xenia and Caron, Fran\c ois},
  title = {SNetOC},
  year = {2017},
  bdsk-url-1 = { https://github.com/misxenia/SNetOC}
}