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Learning subgaussian classes : Upper and minimax bounds

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arxiv 1305.4825 v2 pith:ERHXXJSY submitted 2013-05-21 math.ST stat.TH

Guillaume Lecué , Shahar Mendelson This is my paper · ORCID
classification math.STstat.TH
keywords minimaxmodelobtainprocedureregressionsharpsubgaussianunder
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We obtain sharp oracle inequalities for the empirical risk minimization procedure in the regression model under the assumption that the target Y and the model F are subgaussian. The bound we obtain is sharp in the minimax sense if F is convex. Moreover, under mild assumptions on F, the error rate of ERM remains optimal even if the procedure is allowed to perform with constant probability. A part of our analysis is a new proof of minimax results for the gaussian regression model.

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Cited by 3 Pith papers

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