Generalised Bayesian learning algorithms are increasingly popular in machine learning, due to their PAC generalisation properties and flexibility.
Reading the bibliography…
An oracle inequality for quasi-Bayesian nonnegative matrix factorization
P. Alquier and B. Guedj · 1934
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Some studies in machine learning using the game of checkers
A. L. Samuel · 1959
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I-divergence geometry of probability distributions and minimization problems
I. Csiszár · 1975
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Asymptotic evaluation of certain Markov process expectations for large time — III
M. D. Donsker and S. R. S. Varadhan · 1976
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A theory of the learnable
L. G. Valiant · 1984
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Aggregating strategies
V. G. Vovk · 1990
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The Nature of Statistical Learning Theory
V. N. Vapnik · 1995
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A probabilistic theory of pattern recognition , volume 31
Luc Devroye, László Györfi, and Gábor Lugosi · 1996
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A PAC analysis of a Bayes estimator
J. Shawe-Taylor and R. C. Williamson · 1997
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Convergence rates of posterior distributions
S. Ghosal, J. K. Ghosh, and A. Van Der Vaart · 2000
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Topics in non-parametric statistics
A. Nemirovski · 2000
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PAC-Bayesian generalization bounds for gaussian processes
M. Seeger · 2002
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An introduction to MCMC for machine learning
C. Andrieu, N. De Freitas, A. Doucet, and M. I. Jordan · 2003
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A PAC-Bayesian approach to adaptive classification, 2003
O. Catoni · 2003
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PAC-Bayes & margins
J. Langford and J. Shawe-Taylor · 2003
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Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations
M. Seeger · 2003
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Optimal rates of aggregation
A. B. Tsybakov · 2003
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Regression with multiple candidate models: selecting or mixing?
Y. Yang · 2003
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Une approche PAC-bayésienne de la théorie statistique de l’apprentissage
J.-Y. Audibert · 2004
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Statistical Learning Theory and Stochastic Optimization
O. Catoni · 2004
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Information theory and statistics: a tutorial
I. Csiszár and P. C. Shields · 2004
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A note on the PAC-Bayesian Theorem
A. Maurer · 2004
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Aggregating regression procedures to improve performance
Y. Yang · 2004
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Transductive and Inductive Adaptive Inference for Regression and Density Estimation
P. Alquier · 2006
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Information theory and mixing least-squares regressions
G. Leung and A. R. Barron · 2006
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Tighter PAC-Bayes bounds
A. Ambroladze, E. Parrado-Hernández, and J. Shawe-taylor · 2007
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Combining PAC-Bayesian and generic chaining bounds
J.-Y. Audibert and O. Bousquet · 2007
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Occam’s hammer
G. Blanchard and F. Fleuret · 2007
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PAC-Bayesian Supervised Classification: The Thermodynamics of Statistical Learning , volume 56 of Lecture notes – Monograph Series
O. Catoni · 2007
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Aggregation by exponential weighting and sharp oracle inequalities
A. S. Dalalyan and A. B. Tsybakov · 2007
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Convergence rates of posterior distributions for non-iid observations
S. Ghosal and A. Van Der Vaart · 2007
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The minimum description length principle
P. D. Grünwald · 2007
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PAC-Bayes bounds for the risk of the majority vote and the variance of the Gibbs classifier
A. Lacasse, F. Laviolette, M. Marchand, P. Germain, and N. Usunier · 2007
Cited alongside, same era.
Concentration inequalities and model selection
P. Massart · 2007
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The Bayesian choice: from decision-theoretic foundations to computational implementation
C. Robert · 2007
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PAC-Bayesian bounds for randomized empirical risk minimizers
P. Alquier · 2008
Cited alongside, same era.
Aggregation by exponential weighting, sharp PAC-Bayesian bounds and sparsity
A. S. Dalalyan and A. B. Tsybakov · 2008
Cited alongside, same era.
Graphical models, exponential families, and variational inference
M. J. Wainwright and M. I. Jordan · 2008
Cited alongside, same era.
Fast learning rates in statistical inference through aggregation
J.-Y. Audibert · 2009
Cited alongside, same era.
PAC-Bayesian learning of linear classifiers
P. Germain, A. Lacasse, F. Laviolette, and M. Marchand · 2009
Cited alongside, same era.
An aggregator point of view on NL-Means
J. Salmon and E. Le Pennec · 2009
Cited alongside, same era.
Agrégation PAC-Bayésienne et bandits à plusieurs bras
J.-Y. Audibert · 2010
Cited alongside, same era.
A PAC-Bayes bound for tailored density estimation
M. Higgs and J. Shawe-Taylor · 2010
Cited alongside, same era.
PAC-Bayesian collective stability
B. London, B. Huang, B. Taskar, and L. Getoor · 2014
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Understanding machine learning: From theory to algorithms
S. Shalev-Shwartz and S. Ben-David · 2014
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Généralisations de la théorie PAC-bayésienne pour l’apprentissage inductif, l’apprentissage transductif et l’adaptation de domaine
P. Germain · 2015
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Bayesian reinforcement learning: A survey
M. Ghavamzadeh, S. Mannor, J. Pineau, and A. Tamar · 2015
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A Bayesian approach for noisy matrix completion: Optimal rate under general sampling distribution
T. T. Mai and P. Alquier · 2015
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Learning without concentration
S. Mendelson · 2015
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Fast rates in statistical and online learning
T. van Erven, P. D. Grünwald, N. A. Mehta, M. D. Reid, and R. C. Williamson · 2015
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On the properties of variational approximations of Gibbs posteriors
P. Alquier, J. Ridgway, and N. Chopin · 2016
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On Markov chain Monte Carlo methods for tall data
R. Bardenet, A. Doucet, and C. Holmes · 2016
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Pac-bayesian bounds based on the rényi divergence
L. Bégin, P. Germain, F. Laviolette, and J.-F. Roy · 2016
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PAC-Bayesian bounds for the Gram matrix and least squares regression with a random design
O. Catoni · 2016
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Sub-Gaussian mean estimators
L. Devroye, M. Lerasle, G. Lugosi, and R. I. Oliveira · 2016
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Fast learning rates with heavy-tailed losses
V. C. Dinh, L. S. Ho, B. Nguyen, and D. Nguyen · 2016
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Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes
P. D. Grünwald and N. A. Mehta · 2016
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Risk minimization by median-of-means tournaments
G. Lugosi and S. Mendelson · 2016
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Variational inference: A review for statisticians
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe · 2017
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Theoretical guarantees for approximate sampling from smooth and log-concave densities
A. S. Dalalyan · 2017
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Computing nonvacuous generalization bounds for deep (stochastic) neural networks with many more parameters than training data
G. K. Dziugaite and D. M. Roy · 2017
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Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it
P. D. Grünwald and T. Van Ommen · 2017
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PAC-Bayesian high dimensional bipartite ranking
B. Guedj and S. Robbiano · 2017
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A PAC-Bayesian analysis of randomized learning with application to stochastic gradient descent
B. London · 2017
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Exploring generalization in deep learning
B. Neyshabur, S. Bhojanapalli, D. A. McAllester, and N. Srebro · 2017
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Multi-view learning overview: Recent progress and new challenges
J. Zhao, X. Xie, X. Xu, and S. Sun · 2017
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Simpler PAC-Bayesian bounds for hostile data
P. Alquier and B. Guedj · 2018
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Meta-learning by adjusting priors based on extended PAC-Bayes theory
R. Amit and R. Meir · 2018
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Consistency of variational Bayes inference for estimation and model selection in mixtures
B.-E. Chérief-Abdellatif and P. Alquier · 2018
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Efficient Bayesian computation by proximal Markov Chain Monte Carlo: when Langevin meets Moreau
A. Durmus, E. Moulines, and M. Pereyra · 2018
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Safe probability
P. D. Grünwald · 2018
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A quasi-Bayesian perspective to online clustering
L. Li, B. Guedj, and S. Loustau · 2018
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PAC-Bayes bounds for stable algorithms with instance-dependent priors
O. Rivasplata, E. Parrado-Hernandez, J. Shawe-Taylor, S. Sun, and C. Szepesvari · 2018
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