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The present paper is about estimation and prediction in high-dimensionaladditive models under a sparsity assumption ($p\gg n$ paradigm). A PAC-Bayesianstrategy is investigated, delivering oracle inequalities in probability. Theimplementation is performed through recent outcomes in high-dimensional MCMCalgorithms, and the performance of our method is assessed on simulated data.

PAC-Bayesian Estimation and Prediction in Sparse Additive Models