Julyan Arbel
Présentation
Vous pouvez trouver en suivant les liens: - ma page personnelle: http://www.julyanarbel.com/
Publications
Publications
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Logarithmic Regret for Unconstrained Submodular Maximization Stochastic BanditALT 2025 - 36th International Conference on Algorithmic Learning Theory, Feb 2025, Milan, Italy. pp.1-25, ⟨10.48550/arXiv.2410.08578⟩ |
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Simulation-based inference of yeast centromeresNeurIPS 2025 - 39th Conference on Neural Information Processing Systems Workshop : The 3rd Workshop on Imageomics: Discovering Biological Knowledge from Images Using AI., Dec 2025, Copenhagen, Denmark. pp.1-13, ⟨10.48550/arXiv.2509.00200⟩ |
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Error analysis of a compositional score-based algorithm for simulation-based inferenceWorkshop on Principles of Generative Modeling at EurIPS 2025, Dec 2025, Copenhague, Denmark |
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Some Bayesian nonparametric ideas in (Bayesian) deep learningRSS 2025 - International Conference of Royal Statistical Society, Sep 2025, Edimbourg, United Kingdom |
Overview and challenges in Bayesian deep learningJdS 2025 - 56es Journées de Statistique de la SFdS, Jun 2025, Marseille, France |
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Bayesian deep learning: Overview and challengesBNP 14 - 14th International Conference on Bayesian Nonparametrics, Jun 2025, Los Angeles (CA), United States |
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Clustering inconsistency for Pitman--Yor mixture models with a prior on the precision but fixed discount parameterAABI 2023 - 5th Symposium on Advances in Approximate Bayesian Inference, Jul 2023, Honolulu, United States. pp.1-12 |
Reparameterization of extreme value framework for improved Bayesian workflowEVA 2023 - 13th International Conference on Extreme Value Analysis, Probabilistic and Statistical Models and their Applications, Jun 2023, Milan, Italy |
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Regularized partial least squares for extreme valuesCMStatistics 2023 - 16th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2023, Berlin, Germany |
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Imposing Gaussian Pre-Activations in a Neural NetworkJDS 2022 - 53es Journées de Statistique de la Société Française de Statistiques (SFdS), Jun 2022, Lyon, France |
Improving MCMC convergence diagnostic with a local version of R-hatCMStatistics 2022 - 15th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2022, London, United Kingdom |
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Bayesian nonparametric mixtures inconsistency for the number of clusters53es journées de Statistiques, Société Française de Statistique, Jun 2022, Lyon, France |
On the consistency of Bayesian nonparametric mixtures for the number of clustersISBA 2022 - World Meeting International Society for Bayesian Analysis, Jun 2022, Montreal, Canada |
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On the use of a local R-hat to improve MCMC convergence diagnosticEnergy Forecasting Innovation Conference 2022, May 2022, Londres, United Kingdom |
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Bayesian nonparametric mixture of experts for high-dimensional inverse problemsBNP13 - 13th International Conference on Bayesian Nonparametrics, Oct 2022, Puerto Varas, Chile |
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Mixture of expert posterior surrogates for approximate Bayesian computationSFdS 2022 - 53èmes Journées de Statistique de la Société Française de Statistique, Jun 2022, Lyon, France. pp.1-6 |
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A local version of R-hat for MCMC convergence diagnosticSFdS 2022 - 53èmes Journées de Statistique de la Société Française de Statistique, Jun 2022, Lyon, France. pp.1-6 |
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A Bayesian Framework for Poisson Process Characterization of Extremes with Objective PriorISBA 2021 - World Meeting of the International Society for Bayesian Analysis, Jun 2021, Virtual, France |
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Dependence between Bayesian neural network unitsBDL 2021 - Workshop. Bayesian Deep Learning NeurIPS, Dec 2021, Montreal, Canada. pp.1-9, ⟨10.48550/arXiv.2111.14397⟩ |
A Bayesian framework for Poisson process characterization of extremes with uninformative priorCMStatistics 2021 - 14th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2021, London, United Kingdom |
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Improving MCMC convergence diagnostic with a local version of R-hatMAS 2021 - Journées Modélisation Aléatoire et Statistique, Aug 2021, Orléans, France |
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On Reparameterisations of the Poisson Process Model for Extremes in a Bayesian FrameworkJDS 2021 - 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), Jun 2021, Nice / Virtual, France. pp.1-6 |
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Bayesian neural network unit priors and generalized Weibull-tail propertyACML 2021 - 13th Asian Conference on Machine Learning, Nov 2021, Virtual, Unknown Region. pp.1-16, ⟨10.48550/arXiv.2110.02885⟩ |
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Approximating the clusters' prior distribution in Bayesian nonparametric modelsAABI 2020 - 3rd Symposium on Advances in Approximate Bayesian Inference, Jan 2021, Online, United States. pp.1-16 |
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Bayesian block-diagonal graphical models via the Fiedler priorSFdS - 52 Journées de Statistique de la Société Francaise de Statistique, Jun 2021, Nice, France. pp.1-6 |
Approximate Bayesian computation with surrogate posteriorsISBA 2021 - World Meeting of the International Society for Bayesian Analysis, Jun 2021, Marseille, France |
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Generalized Weibull-tail distributionsJDS 2021 - 52èmes Journées de Statistique de la Société Française de Statistique (SFdS), Jun 2021, Nice, France. pp.1-6 |
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Dictionary-based Learning in MR Fingerprinting: Statistical Learning versus Deep LearningISMRM 2020 - International Society for Magnetic Resonance in Medicine, Aug 2020, Sidney, Australia. pp.1-4 |
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Estimation de paramètres IRM en grande dimension via une régression inverseSFRMBM 2020 - 4e congrés de la Société Française de Résonance Magnétique en Biologie et Médecine, Mar 2020, Strasbourg, France. pp.1 |
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Understanding Priors in Bayesian Neural Networks at the Unit LevelICML 2019 - 36th International Conference on Machine Learning, Jun 2019, Long Beach, United States. pp.6458-6467 |
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Quantitative MRI characterization of brain abnormalities in de novo Parkinsonian patientsISBI 2019 - IEEE International Symposium on Biomedical Imaging, Apr 2019, Venice, Italy. pp.1-4, ⟨10.1109/ISBI.2019.8759544⟩ |
Dependence properties and Bayesian inference for asymmetric multivariate copulasCMStatistics 2019 - 12th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2019, London, United Kingdom |
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Dictionary learning via regression: vascular MRI applicationCNIV 2019 - 3e Congrès National d’Imagerie du Vivant, Feb 2019, Paris, France. pp.1-12 |
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Bayesian Nonparametric Priors for Hidden Markov Random Fields50e Journées de la Statistique de la SFdS, May 2018, Saclay, France. pp.1-5 |
Non parametric Bayesian priors for hidden Markov random fields: application to image segmentationBNPSI 2018 : Workshop on Bayesian non parametrics for signal and image processing, Jul 2018, Bordeaux, France |
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Bayesian neural networks become heavier-tailed with depthNeurIPS 2018 - Thirty-second Conference on Neural Information Processing Systems, Dec 2018, Montréal, Canada. pp.1-7 |
Some distributional properties of Bayesian neural networksWorkshop on Bayesian nonparametrics, Jul 2018, Bordeaux, France |
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A Bayesian Nonparametric Approach to Ecological Risk AssessmentSMPGD 2018 - Workshop on Statistical Methods for Post Genomic Data, Jan 2018, Montpellier, France |
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Bayesian neural network priors at the level of unitsAABI 2018 - 1st Symposium on Advances in Approximate Bayesian Inference, Dec 2018, Montréal, Canada. pp.1-6 |
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Non parametric Bayesian priors for hidden Markov random fieldsJSM 2018 - Joint Statistical Meeting, Jul 2018, Vancouver, Canada. pp.1-38 |
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Dictionary-Free MR Fingerprinting Parameter Estimation Via Inverse RegressionJoint Annual Meeting ISMRM-ESMRMB 2018, Jun 2018, Paris, France. pp.1-2 |
Introduction to Bayesian nonparametric statisticsSéminaire de Statistique au sommet de Rochebrune, Mar 2018, Megève, France |
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Bayesian nonparametric mixture models and clusteringWorkshop 'New challenges in statistics for social sciences', Oct 2017, Venise, Italy |
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Approximating predictive probabilities of Gibbs-type priorsERCIM - 10th International Conference of the ERCIM WG on Computational and Methodological Statistics, Dec 2017, London, United Kingdom |
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Les écoles d'astrostatistique " Statistics for AstrophysicsCFIES 2017 - 5ème Colloque Francophone International sur l’Enseignement de la Statistique, Sep 2017, Grenoble, France |
Bayesian nonparametric clusteringSchool of Statistics for Astrophysics: Bayesian methodology, Oct 2017, Autrans, France |
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Probabilités de découverte d'espèces: Bayes à la rescousse de Good & TuringJournées Scientifiques d'Inria, Jun 2017, Sophia Antipolis, France |
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Investigating predictive probabilities of Gibbs-type priorsMathematical Methods of Modern Statistics, Jul 2017, Marseille, France |
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Bayesian nonparametric inference for discovery probabilitiesYES VIII Workshop on Uncertainty Quantification, Jan 2017, Eindhoven, Netherlands |
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Sequential Quasi Monte Carlo for Dirichlet Process Mixture ModelsNIPS - Conference on Neural Information Processing Systems, Dec 2016, Barcelone, Spain |
Truncation error of a superposed gamma process in a decreasing order representationNIPS Meeting, Dec 2016, Barcelone, Spain |
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On diversity under a Bayesian nonparametric dependent modelXLVII Meeting of the Italian Statistical Society, Italian Statistical Society, Jun 2014, Cagliari, Italy |
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Bayesian mixture models (in)consistency for the number of clusters13th Bayesian nonparametrics (BNP) conference, Oct 2022, Puerto Varas, Chile. |
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A Local Version of R to Improve MCMC Convergence DiagnosticBAYSM 2022 - Bayesian Young Statisticians Meeting, Jun 2022, Montréal, Canada |
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A Local Version of R-hat to Improve MCMC Convergence DiagnosticISBA 2022 - World Meeting of the International Society for Bayesian Analysis, Jun 2022, Montréal, Canada. pp.1-1 |
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Improving MCMC convergence diagnostic: a local version of R-hatBayesComp-ISBA workshop: Measuring the quality of MCMC output, Oct 2021, online, France |
Bayesian Nonparametric Priors for Graph Structured Data: Application to Image SegmentationBayes Comp 2020, Jan 2020, Gainesville, United States |
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Bayesian Nonparametric Mixtures Why and How?IFSS 2018 - 2nd Italian-French Statistics Seminar, Sep 2018, Grenoble, France |
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DATASAFE: understanding Data Accidents for TrAffic SAFEty AcknowledgmentsBayesian learning theory for complex data modelling Workshop, Sep 2018, Grenoble, France. pp.1 |
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Bayesian Nonparametric Priors for Hidden Markov Random Fields: Application to Image SegmentationIFSS 2018 - 2nd Italian-French Statistics Seminar, Sep 2018, Grenoble, France. pp.1 |
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Beta and Dirichlet sub-GaussianityBayesian learning theory for complex data modelling Workshop, Sep 2018, Grenoble, France |
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Chinese restaurant process from stick-breaking for Pitman-YorBayesian learning theory for complex data modelling Workshop, Sep 2018, Grenoble, France. pp.1 |
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Bayesian neural network priors at the level of unitsBayesian Statistics in the Big Data Era, Nov 2018, Marseille, France. pp.1 |
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Sequential Quasi Monte Carlo for Dirichlet Process Mixture ModelsBNP 2017 - 11th Conference on Bayesian NonParametrics, Jun 2017, Paris, France. , pp.1 |
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Unsupervised classification in high dimensionEuropean Week of Astronomy and Space Science (EWASS 2017), Jun 2017, Prague, Czech Republic. , 2017 |
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Applications in IndustrySylvia Fruhwirth-Schnatter; Gilles Celeux; Christian P. Robert. Handbook of mixture analysis, CRC press, pp.1-21, 2019, 9781498763813 |
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Clustering Milky Way's Globulars: a Bayesian Nonparametric ApproachStatistics for Astrophysics: Bayesian Methodology, EDP Sciences, pp.113-137, 2018 |
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A Bayesian nonparametric approach to ecological risk assessmentArgiento, R.; Lanzarone, E.; Antoniano Villalobos, I.; Mattei, A. Bayesian Statistics in Action, 194, , pp.151--159, 2017, Bayesian Statistics in Action |
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Truncation error of a superposed gamma process in a decreasing order representationArgiento, R.; Lanzarone, E.; Antoniano Villalobos, I.; Mattei, A. Bayesian Statistics in Action, 194, Springer; Cham, pp.11--19, 2017, Bayesian Statistics in Action, ⟨10.1007/978-3-319-54084-9_2⟩ |
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Bayesian Survival Model based on Moment CharacterizationSylvia Frühwirth-Schnatter, Angela Bitto, Gregor Kastner, Alexandra Posekany. Bayesian Statistics from Methods to Models and Applications, 126, , pp.3-14, 2015, Springer Proceedings in Mathematics & Statistics, 978-3-319-16238-6. ⟨10.1007/978-3-319-16238-6_1⟩ |
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Contributions to Bayesian nonparametric statisticGeneral Mathematics [math.GM]. Université Paris Dauphine - Paris IX, 2013. English. ⟨NNT : 2013PA090066⟩ |
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Bayesian Statistical Learning and ApplicationsMethodology [stat.ME]. Université grenoble Alpes, CNRS, Institut des Géosciences et de l'Environnement, 2019 |