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General theory for Big Bayes

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Flexible estimation of temporal point processes and graphs

Author(s): Deborah Sulem
Published in: 2023
Publisher: University of Oxfod

Advances in Bayesian asymptotics and Bayesian nonparametrics

Author(s): Caroline Lawless
Published in: 2023
Publisher: University of Oxford

Bayesian modelling of dependent data

Author(s): Daniel Moss
Published in: 2023
Publisher: University of Oxford

Bayesian nonparametric estimation under the manifold hypothesis

Author(s): Paul Rosa
Published in: 2025
Publisher: University of Oxford

Contributions à la sélection bayésienne des modèles de mélange finis et infinis avec une application au calcul distribué

Author(s): Adrien Hairault
Published in: 2023
Publisher: Hal portal theses

Bayesian methods for statistical network analysis and data subsamplingAbstract:

Author(s): Cian Naik
Published in: 2022
Publisher: University of Oxford

Power-law phenomena in Bayesian nonparametrics

Author(s): Francesca Panero
Published in: 2022
Publisher: University of Oxford

Nonparametric Bayesian intensity estimation for covariate-driven inhomogeneous point processes

Author(s): Matteo Giordano; Alisa Kirichenko; Judith Rousseau
Published in: 2024
Publisher: Arxiv

Mean-field Behaviour of Neural Tangent Kernel for Deep Neural Networks (opens in new window)

Author(s): Soufiane Hayou, Arnaud Doucet, Judith Rousseau
Published in: 2020
Publisher: arxiv
DOI: 10.48550/arxiv.1905.13654

Scalable and adaptive variational Bayes methods for Hawkes processes

Author(s): Déborah Sulem, Vincent Rivoirard and Judith Rousseau
Published in: 2022
Publisher: arxiv

Minimax rates without the fixed sample size assumption (opens in new window)

Author(s): Alisa Kirichenko, Peter Grünwald
Published in: 2020
Publisher: Arxiv
DOI: 10.48550/arxiv.2006.11170

Evidence estimation in finite and infinite mixture models and applications (opens in new window)

Author(s): Adrien Hairault; Christian P. Robert, Judith Rousseau
Published in: 2022
Publisher: arxiv
DOI: 10.48550/arxiv.2205.05416

An Equivalence between Bayesian Priors and Penalties in Variational Inference

Author(s): Pierre Wolinski, Guillaume Charpiat, Yann Ollivier
Published in: 2021
Publisher: Arxiv

Asymptotics of approximate Bayesian computation when summary statistics converge at heterogeneous rates (opens in new window)

Author(s): Caroline Lawless; Christian P. Robert; Judith Rousseau; Robin J. Ryder
Published in: 2023
Publisher: arxiv
DOI: 10.48550/arxiv.2311.10080

Empirical Bayes in Bayesian learning: understanding a common practice

Author(s): Stefano Rizzelli, Judith Rousseau, Sonia Petrone
Published in: 2024
Publisher: arxiv

The Bernstein-von Mises theorem for Semiparametric Mixtures

Author(s): Stefan Franssen, Jeanne Nguyen, Aad van der Vaart
Published in: 2024
Publisher: arxiv

Nonparametric regression on random geometric graphs sampled from submanifolds

Author(s): Paul Rosa , Judith Rousseau
Published in: 2024
Publisher: arxiv

Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions

Author(s): Iskander Azangulov, George Deligiannidis, Judith Rousseau
Published in: 2024
Publisher: arxiv

Ideal Bayesian Spatial Adaptation (opens in new window)

Author(s): Veronicka Rockova; Judith Rousseau
Published in: Journal of the American Statistical Association, 2023, ISSN 0162-1459
Publisher: American Statistical Association
DOI: 10.1080/01621459.2023.2241705

Efficient Bayesian estimation and use of cut posterior in semiparametric hidden Markov models (opens in new window)

Author(s): Daniel Moss, Judith Rousseau
Published in: Electronic Journal of Statistics, Issue 18, 2024, ISSN 1935-7524
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/23-ejs2201

Asymptotic analysis of statistical estimators related to MultiGraphex processes under misspecification (opens in new window)

Author(s): Zacharie Naulet, Judith Rousseau, François Caron
Published in: Bernoulli, Issue 30, 2024, ISSN 1350-7265
Publisher: Chapman & Hall
DOI: 10.3150/23-bej1689

Sparse networks with core-periphery structure (opens in new window)

Author(s): Cian Naik, Caron François, Judith Rousseau
Published in: Electronic Journal of Statistics, Issue 15, 2021, Page(s) 1814-1868, ISSN 1935-7524
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/21-ejs1819

Semiparametric posterior corrections (opens in new window)

Author(s): Andrew Yiu, Edwin Fong, Chris Holmes, Judith Rousseau
Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology, 2025, ISSN 1369-7412
Publisher: Blackwell Publishing Inc.
DOI: 10.1093/jrsssb/qkaf005

On sparsity, power-law and clustering properties of graphex processes (opens in new window)

Author(s): F. Caron, F. Panero and J. Rousseau
Published in: Advances in applied probability, 2023, ISSN 0001-8678
Publisher: Applied Probability Trust
DOI: 10.1017/apr.2022.75

Wasserstein convergence in Bayesian and frequentist deconvolution models (opens in new window)

Author(s): Judith Rousseau, Catia Scricciolo
Published in: The Annals of Statistics, Issue 52, 2024, ISSN 0090-5364
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/24-aos2413

Bayesian estimation of nonlinearHawkes process (opens in new window)

Author(s): D. Sulem, V. Rivoirard and J. Rousseau
Published in: Bernoulli, 2023, ISSN 1350-7265
Publisher: Chapman & Hall
DOI: 10.3150/23-bej1631

Estimating a density near an unknown manifold: A Bayesian nonparametric approach (opens in new window)

Author(s): Clément Berenfeld, Paul Rosa, Judith Rousseau
Published in: The Annals of Statistics, Issue 52, 2024, ISSN 0090-5364
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/24-aos2423

Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19 (opens in new window)

Author(s): Raiha Browning, Deborah Sulem, Kerrie Mengersen, Vincent Rivoirard, Judith Rousseau
Published in: PLOS ONE, Issue 16/4, 2021, Page(s) e0250015, ISSN 1932-6203
Publisher: Public Library of Science
DOI: 10.1371/journal.pone.0250015

Safe-Bayesian Generalized Linear Regression

Author(s): Rianne de Heide, Alisa Kirichenko, Nishant Mehta, Peter Grünwald
Published in: AISTATs, 2020
Publisher: MLR

Bayesian nonparametrics for sparse dynamic networks (opens in new window)

Author(s): Cian Naik, Francois Caron, Judith Rousseau, Yee Whye Teh, Konstantina Palla
Published in: European Conference on Machine Learning and Data Mining, 2022
Publisher: ECML PKDD
DOI: 10.48550/arxiv.1607.01624

On the inability of Gaussian process regression to optimally learn compositional functions (opens in new window)

Author(s): Matteo Giordano, Johannes Schmidt-Hieber, Kolyan Ra
Published in: Advances in Neural Information Processing Systems 36, Issue 10495258, 2022, ISSN 1049-5258
Publisher: NeurIPS
DOI: 10.48550/arxiv.2205.07764

The Curse of Depth in Kernel Regime

Author(s): Soufiane Hayou;Arnaud Doucet; Judith Rousseau
Published in: 2021
Publisher: PMLR

Gaussian Pre-Activations in Neural Networks: Myth or Reality?

Author(s): Pierre Wolinsky, Julyan Arbel
Published in: Transactions on Machine Learning Research, 2025, ISSN 2835-8856
Publisher: JMLR

Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds (opens in new window)

Author(s): Paul Rosa; Viacheslav Borovitskiy; Alexander Terenin; Judith Rousseau;
Published in: NeurIPS, 2023
Publisher: Open Review.net
DOI: 10.48550/arxiv.2309.10918

Stable Resnet

Author(s): Hayou, Soufiane and Clerico, Eugenio and He, Bobby and Deligiannidis, George and Doucet, Arnaud and Rousseau, Judith
Published in: Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, Issue 130, 2021, Page(s) 1324--1332
Publisher: PMLR

Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement

Author(s): Cian Naik; Trevor Campbell; Judith Rousseau
Published in: NeurIPS, 2022
Publisher: OpenReview.net

Besov-Laplace priors in density estimation: optimal posterior contraction rates and adaptation (opens in new window)

Author(s): Matteo Giordano
Published in: Electronic Journal of Statistics, Issue 17, 2023, ISSN 1935-7524
Publisher: Institute of Mathematical Statistics
DOI: 10.1214/23-ejs2161

Nonparametric Bayesian Inference for Reversible Multi-Dimensional Diffusions (opens in new window)

Author(s): Matteo Giordano; Kolyan Ray
Published in: The Annals of Statistics, Issue 00905364, 2022, ISSN 0090-5364
Publisher: Institute of Mathematical Statistics
DOI: 10.48550/arxiv.2012.12083

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