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In this paper, we explore adaptive inference based on variational Bayes.
Optimal global rates of convergence for nonparametric regression
Charles J Stone · 1982
Earlier work this paper cites.
Additive regression and other nonparametric models
Charles J Stone · 1985
Earlier work this paper cites.
Asymptotic methods in statistical decision theory
Lucien Le Cam · 1986
Earlier work this paper cites.
Linear smoothers and additive models
Andreas Buja, Trevor Hastie, and Robert Tibshirani · 1989
Earlier work this paper cites.
Convergence rates of posterior distributions
Subhashis Ghosal, Jayanta K Ghosh, and Aad van der Vaart · 2000
Earlier work this paper cites.
Bayesian aspects of some nonparametric problems
Linda H Zhao · 2000
Earlier work this paper cites.
On Bayesian consistency
Stephen Walker and Nils Lid Hjort · 2001
Earlier work this paper cites.
On Bayesian consistency
Stephen Walker and Nils Lid Hjort · 2001
Earlier work this paper cites.
Adaptive Bayesian inference on the mean of an infinite-dimensional normal distribution
Eduard Belitser and Subhashis Ghosal · 2003
Earlier work this paper cites.
Statistical learning theory and stochastic optimization: Ecole d’Eté de Probabilités de Saint-Flour, XXXI-2001 , volume 1851
Olivier Catoni · 2004
Earlier work this paper cites.
Pattern recognition and machine learning , volume 4
Christopher M Bishop and Nasser M Nasrabadi · 2006
Earlier work this paper cites.
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Tong Zhang · 2006
Earlier work this paper cites.
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Christopher M Bishop and Nasser M Nasrabadi · 2006
Earlier work this paper cites.
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Stéphane Gaiffas and Guillaume Lecué · 2007
Earlier work this paper cites.
Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions
Joel L Horowitz and Enno Mammen · 2007
Earlier work this paper cites.
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Jüri Lember and Aad van der Vaart · 2007
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Yu A Kutoyants · 2012
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Bayesian empirical likelihood for quantile regression
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Tony Cai, Zongming Ma, and Yihong Wu · 2015
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Minimax-optimal nonparametric regression in high dimensions
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Rate-optimal posterior contraction for sparse PCA
Chao Gao and Harrison H Zhou · 2015
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Marc Hoffmann, Judith Rousseau, and Johannes Schmidt-Hieber · 2015
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Pierre Alquier, James Ridgway, and Nicolas Chopin · 2016
Earlier work this paper cites.
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Pierre Alquier, James Ridgway, and Nicolas Chopin · 2016
Earlier work this paper cites.
Rate exact Bayesian adaptation with modified block priors
Chao Gao and Harrison H Zhou · 2016
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Fast Bayesian factor analysis via automatic rotations to sparsity
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Fundamentals of nonparametric Bayesian inference , volume 44
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Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator
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