How can we select the best performing data-driven model? How can we rigorously estimate its generalization error? Statistical learning theory (SLT) answers these questions by deriving nonasymptotic bounds on the generalization error of a model or, in other words, by delivering upper bounding of the true error of the learned model based just on...
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2018 (v1)PublicationUploaded on: April 14, 2023
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2017 (v1)Publication
In this paper we deal with the problem of improving the recent milestone results on the estimation of the generalization capability of a randomized learning algorithm based on Differential Privacy (DP). In particular, we derive new DP based multiplicative Chernoff and Bennett type generalization bounds, which improve over the current...
Uploaded on: April 14, 2023