Publications
Publications, discussions and preprints
[59]
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Jose Miguel Hernandez Lobato, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, and Ruqi Zhang. Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. Preprint, submitted, 2024. [ arXiv ]
[58]
Mathias Barreto, Olivier Marchal, and Julyan Arbel. Optimal sub-Gaussian variance proxy for truncated Gaussian and exponential random variables. Preprint, submitted, 2024. [ arXiv | HAL ]
[57]
Julien Zhou, Pierre Gaillard, Thibaud Rahier, Houssam Zenati, and Julyan Arbel. Covariance-adaptive least-squares algorithm for stochastic combinatorial semi-bandits. Preprint, submitted, 2024. [ arXiv | HAL ]
[56]
Julyan Arbel, Konstantinos Pitas, Mariia Vladimirova, and Vincent Fortuin. A primer on Bayesian neural networks: review and debates. Preprint, submitted, 2023. [ arXiv ]
[55]
Julyan Arbel, Stéphane Girard, and Hadrien Lorenzo. Shrinkage for Extreme Partial Least Squares. Preprint, submitted, 2023. [ arXiv | code | HAL ]
[54]
Konstantinos Pitas and Julyan Arbel. The fine print on tempered posteriors. Asian Conference on Machine Learning, 2023. [ arXiv ]
[53]
Julyan Arbel, Hong-Phuong Dang, Clément Elvira, Cédric Herzet, Zacharie Naulet, and Mariia Vladimirova. Bayes in action in deep learning and dictionary learning. ESAIM: Proceedings and Surveys, 74:90--107, 2023. [ DOI | X | HAL | journal ]
[52]
Minh Tri Lê, Pierre Wolinski, and Julyan Arbel. Efficient neural networks for tiny machine learning: A comprehensive review. Preprint, submitted, 2023. [ arXiv | HAL ]
[51]
Théo Moins, Julyan Arbel, Anne Dutfoy, and Stéphane Girard. On the use of a local R to improve MCMC convergence diagnostic. Bayesian Analysis, 2023. [ arXiv | code | HAL ]
[50]
Théo Moins, Julyan Arbel, Stéphane Girard, and Anne Dutfoy. Reparameterization of extreme value framework for improved Bayesian workflow. Computational Statistics & Data Analysis, 2023. [ DOI | arXiv | HAL | journal | http ]
[49]
Caroline Lawless, Julyan Arbel, Louise Alamichel, and Guillaume Kon Kam King. Clustering inconsistency for Pitman--Yor mixture models with a prior on the precision but fixed discount parameter. Fifth Symposium on Advances in Approximate Bayesian Inference, 2023. [ http ]
[48]
Hien Nguyen, Trungtin Nguyen, Julyan Arbel, and Florence Forbes. Concentration results for approximate Bayesian computation without identifiability. Preprint, submitted, 2023. [ HAL ]
[47]
Pierre Wolinski and Julyan Arbel. Gaussian Pre-Activations in Neural Networks: Myth or Reality? Preprint, submitted, 2023. [ arXiv | HAL ]
[46]
Florian Privé, Clara Albiñana, Julyan Arbel, Bogdan Pasaniuc, and Bjarni J Vilhjálmsson. Inferring disease architecture and predictive ability with LDpred2-auto. bioRxiv, 2023. [ DOI | X | pdf ]
[45]
Louise Alamichel, Daria Bystrova, Julyan Arbel, and Guillaume Kon Kam King. Bayesian mixture models (in)consistency for the number of clusters. Preprint, submitted, 2022. [ HAL | http ]
[44]
Julyan Arbel, Stéphane Girard, Hien Nguyen, and Antoine Usseglio-Carleve. Multivariate expectile-based distribution: properties, Bayesian inference and applications. Journal of Statistical Planning and Inference, 2022. [ DOI | HAL | journal ]
[43]
Florence Forbes, Hien Duy Nguyen, Trung Tin Nguyen, and Julyan Arbel. Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors. Statistics and Computing, 32, 2022. [ DOI | video | HAL | journal ]
[42]
Florian Privé, Julyan Arbel, Hugues Aschard, and Bjarni J. Vilhjálmsson. Identifying and correcting multiple sources of misspecification in GWAS summary statistics for polygenic scores. Human Genetics and Genomics Advances, 3, 2022. [ DOI | code | X | journal ]
[41]
Daria Bystrova, Giovanni Poggiato, Julyan Arbel, and Wilfried Thuiller. Latent Factor Models: A Tool for Dimension Reduction in Joint Species Distribution Models, chapter 7, pages 135--156. John Wiley & Sons, Ltd, 2022. [ DOI | arXiv | code | HAL | journal | http ]
[40]
Mariia Vladimirova, Julyan Arbel, and Stéphane Girard. Bayesian neural network unit priors and generalized Weibull-tail property. Asian Conference on Machine Learning, 2021. [ arXiv ]
[39]
Julyan Arbel and Stefano Favaro. Approximating predictive probabilities of Gibbs-type priors. Sankhya A, (8):496--519, 2021. [ DOI | arXiv | HAL | journal ]
[38]
Julyan Arbel, Guillaume Kon Kam King, Antonio Lijoi, Luis E. Nieto-Barajas, and Igor Prünster. BNPdensity: Bayesian nonparametric mixture modeling in R. Australian & New Zealand Journal of Statistics, 63:542--564, 2021. [ DOI | arXiv ]
[37]
Daria Bystrova, Julyan Arbel, Guillaume Kon Kam King, and François Deslandes. Approximating the clusters' prior distribution in Bayesian nonparametric models. In Third Symposium on Advances in Approximate Bayesian Inference, 2021. [ video | HAL | journal ]
[36]
Giovanni Poggiato, Tamara Münkemüller, Daria Bystrova, Julyan Arbel, James Clark, and Wilfried Thuiller. On the interpretations of joint modelling in community ecology. Trends in Ecology and Evolution, 36(5):391--401, 2021. [ DOI | HAL ]
[35]
Daria Bystrova, Giovanni Poggiato, Billur Bektas, Julyan Arbel, James S Clark, Alessandra Guglielmi, and Wilfried Thuiller. Clustering species with residual covariance matrix in Joint Species Distribution models. Frontiers Ecology And Evolution, 9:128, 2021. [ DOI | code | HAL ]
[34]
Fabien Boux, Florence Forbes, Julyan Arbel, Benjamin Lemasson, and Emmanuel Barbier. Bayesian inverse regression for vascular magnetic resonance fingerprinting. IEEE Transactions on Medical Imaging, 40(7):1827--1837, 2021. [ DOI ]
[33]
Théo Moins, Julyan Arbel, Anne Dutfoy, and Stéphane Girard. Discussion of the paper “Rank-Normalization, Folding, and Localization: An Improved R for Assessing Convergence of MCMC”. Bayesian Analysis, 2021. [ DOI | HAL | journal ]
[32]
Julyan Arbel, Riccardo Corradin, and Bernardo Nipoti. Dirichlet process mixtures under affine transformations of the data. Computational Statistics, 24(1):1--25, 2020. [ DOI | arXiv | code | journal ]
[31]
Hien D Nguyen, Julyan Arbel, Hongliang Lü, and Florence Forbes. Approximate Bayesian computation via the energy statistic. IEEE Access, 2020. [ DOI | arXiv | code | HAL | journal ]
[30]
Mariia Vladimirova, Stéphane Girard, Hien D Nguyen, and Julyan Arbel. Sub-Weibull distributions: generalizing sub-Gaussian and sub-Exponential properties to heavier-tailed distributions. Stat, 2020. [ DOI | arXiv | HAL ]
[29]
Julyan Arbel, Olivier Marchal, and Bernardo Nipoti. On the Hurwitz zeta function with an application to the exponential-beta distribution. Journal of Inequalities and Applications, (89), 2020. [ DOI | arXiv ]
[28]
Hongliang Lü, Julyan Arbel, and Florence Forbes. Bayesian nonparametric priors for hidden Markov random fields. Statistics and Computing, 2020. [ DOI | HAL ]
[27]
Florian Privé, Julyan Arbel, and Bjarni J. Vilhjálmsson. LDpred2: better, faster, stronger. Bioinformatics, 2020. [ DOI | code | X | HAL | journal ]
[26]
Julyan Arbel, Olivier Marchal, and Hien D Nguyen. On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables. ESAIM: Probability & Statistics, 24:39--55, 2020. [ DOI | arXiv | HAL | journal ]
[25]
Daria Bystrova, Julyan Arbel, and Thibaud Rahier. Discussion of “Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects”, by Hahn, Murray, and Carvalho. Bayesian Analysis, 2020. [ DOI | HAL | journal | pdf ]
[24]
Julyan Arbel, Marta Crispino, and Stéphane Girard. Dependence properties and Bayesian inference for asymmetric multivariate copulas. Journal of Multivariate Analysis, 174, 2019. [ DOI | arXiv | code | HAL ]
[23]
Mariia Vladimirova, Jakob Verbeek, Pablo Mesejo, and Julyan Arbel. Understanding Priors in Bayesian Neural Networks at the Unit Level. ICML, 2019. [ arXiv | video | slides | X | journal | suppl | poster award ]
[22]
Caroline Lawless and Julyan Arbel. A simple proof of Pitman--Yor's Chinese restaurant process from its stick-breaking representation. Dependence Modeling, 7, 2019. [ DOI | arXiv | poster | HAL | journal ]
[21]
Julyan Arbel, Pierpaolo De Blasi, and Igor Prünster. Stochastic approximations to the Pitman--Yor process. Bayesian Analysis, 14(3):753--771, 2019. [ DOI | arXiv | HAL | journal ]
[20]
Julyan Arbel, Riccardo Corradin, and Michal Lewandowski. Discussion of “Bayesian Cluster Analysis: Point Estimation and Credible Balls”, by Wade and Ghahramani. Bayesian Analysis, 13:559--626, 2018. [ HAL | journal ]
[19]
Julyan Arbel. Clustering Milky Way's Globulars: a Bayesian Nonparametric Approach, chapter in Statistics for Astrophysics: Bayesian Methodology. EDP Sciences. Editors: Didier Fraix-Burnet, Stéphane Girard, Julyan Arbel and Jean-Baptiste Marquette, 2018. [ HAL ]
[18]
Kerrie Mengersen, Clair Alston, Julyan Arbel, and Earl Duncan. Applications in Industry, chapter in Handbook of mixture analysis. CRC Press, Editors: Gilles Celeux, Sylvia Früwirth-Schnatter, and Christian P. Robert, 2018. [ HAL | journal ]
[17]
Didier Fraix-Burnet, Stéphane Girard, Julyan Arbel, and Jean-Baptiste Marquette, editors. Statistics for Astrophysics: Bayesian Methodology. EDP Sciences, 2018. [ HAL | journal ]
[16]
Olivier Marchal and Julyan Arbel. On the sub-Gaussianity of the Beta and Dirichlet distributions. Electronic Communications in Probability, 22:1--14, 2017. [ DOI | arXiv | poster | HAL | blog ]
[15]
Julyan Arbel and Igor Prünster. A moment-matching Ferguson & Klass algorithm. Statistics and Computing, 27(1):3--17, 2017. [ DOI | arXiv | HAL | journal ]
[14]
Julyan Arbel, Stefano Favaro, Bernardo Nipoti, and Yee Whye Teh. Bayesian nonparametric inference for discovery probabilities: credible intervals and large sample asymptotics. Statistica Sinica, 27:839--858, 2017. [ DOI | arXiv | HAL | journal | blog ]
[13]
Julyan Arbel. Discussion of “Sparse graphs using exchangeable random measures” by Caron and Fox. Journal of the Royal Statistical Society. Series B, 79, 2017. [ DOI | arXiv | pdf ]
[12]
Guillaume Kon Kam King, Julyan Arbel, and Igor Prünster. Bayesian Statistics in Action, chapter A Bayesian nonparametric approach to ecological risk assessment, pages 151--159. Springer Proceedings in Mathematics & Statistics, Volume 194. Springer International Publishing, Editors: Raffaele Argiento et al., 2017. [ DOI | springer ]
[11]
Julyan Arbel and Igor Prünster. Bayesian Statistics in Action, chapter On the truncation error of a superposed gamma process, pages 11--19. Springer Proceedings in Mathematics & Statistics, Volume 194. Springer International Publishing, Editors: Raffaele Argiento et al., 2017. [ DOI | springer ]
[10]
Julyan Arbel, Kerrie Mengersen, and Judith Rousseau. Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity. Annals of Applied Statistics, 10(3):1496--1516, 2016. [ DOI | arXiv | HAL | journal ]
[9]
Julyan Arbel and Vianney Costemalle. Estimation of immigration flows : reconciling two sources by a Bayesian approach (in French). Économie et Statistique, 483-484-485:121--149, 2016. [ HAL | journal | pdf ]
[8]
Julyan Arbel, Antonio Lijoi, and Bernardo Nipoti. Full Bayesian inference with hazard mixture models. Computational Statistics & Data Analysis, 93:359--372, 2016. [ DOI | arXiv | HAL | journal | blog ]
[7]
Julyan Arbel and Christian P. Robert. Discussion of “Statistical modelling of citation exchange between statistics journals” by Varin, Cattelan and Firth. Journal of the Royal Statistical Society. Series A, 179:41--42, 2016. [ DOI | journal | pdf | blog ]
[6]
Julyan Arbel, Kerrie Mengersen, Ben Raymond, Tristrom Winsley, and Catherine King. Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel contaminated soil. Ecology and Evolution, 5(13):2633--2645, 2015. [ DOI | HAL | journal | pdf ]
[5]
Julyan Arbel and Igor Prünster. Discussion of “Sequential Quasi-Monte Carlo” by Gerber and Chopin. Journal of the Royal Statistical Society. Series B, 77:559--560, 2015. [ DOI | arXiv | HAL ]
[4]
Julyan Arbel, Antonio Lijoi, and Bernardo Nipoti. Bayesian Statistics from Methods to Models and Applications, chapter Bayesian Survival Model based on Moment Characterization, pages 3--14. Springer Proceedings in Mathematics & Statistics, Volume 126. Springer International Publishing, Editors: Sylvia Frühwirth-Schnatter et al., 2015. [ DOI | arXiv | HAL | blog | springer ]
[3]
Julyan Arbel, Ghislaine Gayraud, and Judith Rousseau. Bayesian optimal adaptive estimation using a sieve prior. Scandinavian Journal of Statistics, 40(3):549--570, 2013. [ DOI | arXiv | HAL | journal ]
[2]
Julyan Arbel and Bernardo Nipoti. Discussion of “Bayesian Nonparametric Inference--Why and How” by Müller and Mitra. Bayesian Analysis, 8(02):326--328, 2013. [ pdf ]
[1]
Christian P. Robert and Julyan Arbel. Discussion of “Sparse Bayesian regularization and prediction” by Polson and Scott. Bayesian Statistics 9, 2009. [ DOI | book | pdf ]
Proceedings, theses, etc
[26]
Julyan Arbel, Mario Beraha, and Daria Bystrova. Bayesian block-diagonal graphical models via the Fiedler prior. In JDS 2021 - 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), pages 1--6, Nice, France, June 2021. [ HAL | pdf | http ]
[25]
Théo Moins, Julyan Arbel, Anne Dutfoy, and Stéphane Girard. On Reparameterisations of the Poisson Process Model for Extremes in a Bayesian Framework. In JDS 2021 - 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), pages 1--6, Nice / Virtual, France, June 2021. [ HAL | pdf | http ]
[24]
Mariia Vladimirova, Julyan Arbel, and Stéphane Girard. Generalized Weibull-tail distributions. In JDS 2021 - 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), pages 1--6, Nice, France, June 2021. [ HAL | pdf | http ]
[23]
Mariia Vladimirova, Julyan Arbel, and Stéphane Girard. Dependence between Bayesian neural network units. In Bayesian Deep Learning workshop, NeurIPS, 2021. [ arXiv ]
[22]
Mariia Vladimirova, Julyan Arbel, and Stéphane Girard. Dependence between Bayesian neural network units. In Bayesian Deep Learning Workshop, 2021. [ arXiv | twitter | journal ]
[21]
Julyan Arbel. Bayesian Statistical Learning and Applications. HDR thesis, Université Grenoble-Alpes, 2019. [ HAL ]
[20]
Veronica Munoz Ramirez, Florence Forbes, Julyan Arbel, Alexis Arnaud, and Michel Dojat. Quantitative MRI Characterization of Brain Abnormalities in `de novo' Parkinsonian Patients. In IEEE International Symposium on Biomedical Imaging (ISBI), 2019. [ HAL ]
[19]
Fabien Boux, Florence Forbes, Julyan Arbel, and Emmanuel Barbier. Estimation de paramètres IRM en grande dimension via une reégression inverse. In Congrès de la Société Française de Résonance Magnétique en Biologie et Médecine (SFRMBM), 2019.
[18]
Fabien Boux, Florence Forbes, Julyan Arbel, and Emmanuel Barbier. Dictionary learning via regression: vascular MRI application. In Congrès National de l'Imagerie du Vivant (CNIV), 2019. [ HAL ]
[17]
Fabien Boux, Florence Forbes, Julyan Arbel, and Emmanuel Barbier. Dictionary-free MR fingerprinting parameter estimation via inverse regression. In International Society for Magnetic Resonance in Medicine (ISMRM), 2018. [ HAL ]
[16]
Mariia Vladimirova, Julyan Arbel, and Pablo Mesejo. Bayesian neural networks become heavier-tailed with depth. In NeurIPS Bayesian Deep Learning Workshop, 2018. [ arXiv | journal | pdf ]
[15]
Mariia Vladimirova, Julyan Arbel, and Pablo Mesejo. Bayesian neural network priors at the level of units. In 1st Symposium on Advances in Approximate Bayesian Inference, 2018. [ arXiv | journal | pdf ]
[14]
Hongliang Lü, Julyan Arbel, and Florence Forbes. Bayesian Nonparametric Priors for Hidden Markov Random Fields. In 50e Journées de la Statistique de la SFdS, 2018. [ pdf ]
[13]
Julyan Arbel. Faà di Bruno's note on eponymous formula, trilingual version. arXiv, 2016. [ arXiv | blog ]
[12]
Julyan Arbel and Jean-Bernard Salomond. Sequential Quasi Monte Carlo for Dirichlet Process Mixture Models. In NeurIPS Practical Bayesian Nonparametrics workshop, 2016. [ poster | HAL | journal ]
[11]
Julyan Arbel and Igor Prünster. Truncation error of a superposed gamma process in a decreasing order representation. In NeurIPS Advances in Approximate Bayesian Inference workshop, 2016. [ HAL | journal ]
[10]
Guillaume Kon Kam King, Julyan Arbel, and Igor Prünster. Bayesian Nonparametric Density Estimation in Ecotoxicology. In 48e Journées de la Statistique de la SFdS, 2016. [ slides | pdf ]
[9]
Julyan Arbel, Stefano Favaro, Bernardo Nipoti, and Yee Whye Teh. Discovery Probabilities when Uncertainty Matters. In 48e Journées de la Statistique de la SFdS, 2016. [ pdf ]
[8]
Julyan Arbel, Stefano Favaro, Bernardo Nipoti, and Yee Whye Teh. On Bayesian nonparametric inference for discovery probabilities. In Proceedings of the 48th Meeting of the Italian Statistical Society, 2016.
[7]
Julyan Arbel, Kerrie Mengersen, and Judith Rousseau. On diversity under a Bayesian nonparametric dependent model. In Proceedings of the 47th Meeting of the Italian Statistical Society, 2014. [ HAL | pdf ]
[6]
Julyan Arbel. Contributions to Bayesian nonparametric statistics. PhD thesis, Université Paris-Dauphine, 2013. [ HAL | these.fr | pdf ]
[5]
Julyan Arbel and Pierre Jacob. Analyse critique d'une peinture abstraite. Variances, 42:56--58, 2011. [ journal | pdf ]
[4]
Julyan Arbel. La promotion 2008 tire son épingle du jeu face à la crise. Variances, 36:6--8, 2009. [ journal | pdf ]
[3]
Julyan Arbel. Nonparametric estimation of level sets with simulations, under supervision of Prof. Judith Rousseau and Prof. Ghislaine Gayraud, Université Paris-Dauphine, France. Master's thesis, 2008. [ pdf ]
[2]
Julyan Arbel. Intrinsic Bayesian inference of the positive predictive value, under supervision of Prof. José-Miguel Bernardo, Universitat de València, Spain. Master's thesis, 2007. [ pdf ]
[1]
Julyan Arbel. Arc presentations of knots and links, under supervision of Prof. Hugh Morton, University of Liverpool, United-Kingdom. Stage d'Option de l'École Polytechnique, 2006. [ pdf ]