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Probably approximate Bayesian computation: nonasymptotic convergence of ABC under misspecification
In this paper we develop theoretical bounds for the distance between the statistics used in ABC.
Concentration of tempered posteriors and of their variational approximations
While Bayesian methods are extremely popular in statistics and machine learning, their application to massive datasets is often challenging, when possible at all.
On the properties of variational approximations of Gibbs posteriors
We consider instead variational approximations of the Gibbs posterior, which are fast to compute.
PAC-Bayesian AUC classification and scoring
We also extend our method to a class of non-linear score functions, essentially leading to a nonparametric procedure, by considering a Gaussian process prior.
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