Výsledky vyhledávání - "Todeschini, Adrien"

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Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities

Autor: Todeschini, Adrien, Miscouridou, Xenia, Caron, François

We propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process, and naturally generalizes existing probabilistic models with overlapping blo

Externí odkaz: http://arxiv.org/abs/1602.02114

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Biips: Software for Bayesian Inference with Interacting Particle Systems

Autor: Todeschini, Adrien, Caron, François, Fuentes, Marc, Legrand, Pierrick, Del Moral, Pierre

Biips is a software platform for automatic Bayesian inference with interacting particle systems. Biips allows users to define their statistical model in the probabilistic programming BUGS language, as well as to add custom functions or samplers withi

Externí odkaz: http://arxiv.org/abs/1412.3779

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Akademický článek

Exchangeable random measures for sparse and modular graphs with overlapping communities.

Autor: Todeschini, Adrien¹ (AUTHOR), Miscouridou, Xenia² (AUTHOR), Caron, François² (AUTHOR) caron@stats.ox.ac.uk

Publikováno v: Journal of the Royal Statistical Society: Series B (Statistical Methodology). Apr2020, Vol. 82 Issue 2, p487-520. 34p.

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Compétitions d'apprentissage automatique avec le package R rchallenge

Autor: Todeschini, Adrien, Genuer, Robin

Publikováno v: 47èmes Journées de Statistique de la SFdS
47èmes Journées de Statistique de la SFdS, Société Française de Statistique, Jun 2015, Lille, France

In machine learning, empirical performance on real data are crucial in the success of a method. Recent years have seen the emergence of a large number of machine learning competitions. These challenges are motivated by industrial (Netflix prize) or a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::d8cd2d1bcb2ac68dcf42ee2f3d741f1c
http://hdl.handle.net/20.500.12278/114134

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Dissertation/ Thesis

Probabilistic and Bayesian nonparametric approaches for recommender systems and networks

Autor: Todeschini, Adrien

Nous proposons deux nouvelles approches pour les systèmes de recommandation et les réseaux. Dans la première partie, nous donnons d’abord un aperçu sur les systèmes de recommandation avant de nous concentrer sur les approches de rang faible po

Externí odkaz: http://www.theses.fr/2016BORD0237/document

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Approche bayésienne non paramétrique pour la factorisation de matrice binaire à faible rang avec loi de puissance

Autor: Todeschini, Adrien, Caron, Francois

Publikováno v: 47èmes Journées de Statistique de la SFdS
47èmes Journées de Statistique de la SFdS, Société Française de Statistique, Jun 2015, Lille, France

National audience; We introduce a low-rank Bayesian nonparametric (BNP) model for bipartite graphs. Recently, Caron (2012) proposed a BNP model where each node is given its own sociability parameter allowing to capture the power-law behavior of real

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c17c12354d686045506b975cce6f85f2
https://hal.inria.fr/hal-01157151v2/document

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Complétion de matrice de rang faible probabiliste à l'aide d'algorithmes de régularisation spectrale adaptatifs

Autor: Todeschini, Adrien, Caron, Francois, Chavent, Marie

Publikováno v: 46e Journées de Statistique
46e Journées de Statistique, Société Française de Statistique, Jun 2014, Rennes, France

National audience; Nous proposons une nouvelle classe d'algorithmes pour la complétion de matrice de rang faible. Notre approche s'appuie sur de nouvelles fonctions de pénalité sur les valeurs singulières de la matrice de rang faible. En exploita

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::197085ba4c4c09cf7dd8e66fd152e1b9
http://hdl.handle.net/20.500.12278/114426

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Recent Developments in Software for MCMC - Round Table Session: Biips software: inference in Bayesian graphical models with sequential Monte Carlo methods

Autor: Todeschini, Adrien, Caron, Francois, Fuentes, Marc, Legrand, Pierrick, Del Moral, Pierre

Publikováno v: Fifth IMS-ISBA joint meeting MCMSki IV
Fifth IMS-ISBA joint meeting MCMSki IV, IMS (Institute of Mathematical Statistics) and ISBA (International Society for Bayesian Analysis), Jan 2014, Chamonix Mont-Blanc, France

International audience; The main factor in the success of Markov Chain Monte Carlo Methods is that they can be implemented with little efforts in a large variety of settings. Many softwares have been developped such as BUGS and JAGS, that helped to p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::eaa857debe097ae1cdf9be5e71ab2526
https://hal.inria.fr/hal-01108393

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Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms

Autor: Todeschini, Adrien, Caron, Francois, Chavent, Marie

Publikováno v: NIPS-The Neural Information Processing Systems Conference
NIPS-The Neural Information Processing Systems Conference, The Neural Information Processing Systems (NIPS) Foundation, Inc., Dec 2013, South Lake Tahoe, United States. pp.845-853

International audience; We propose a novel class of algorithms for low rank matrix completion. Our approach builds on novel penalty functions on the singular values of the low rank matrix. By exploiting a mixture model representation of this penalty,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::d4b3e8afa26d628dc0a55d669e440568
http://hdl.handle.net/20.500.12278/114440

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