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Lepskii Principle in Supervised Learning


In the setting of supervised learning using reproducing kernel methods, we propose a data-dependent regularization parameter selection rule that is adaptive to the unknown regularity of the target function and is optimal both for the least-square (prediction) error and for the reproducing kernel Hilbert space (reconstruction) norm error. It is based on a modified Lepskii balancing principle using a varying family of norms.

Dates and versions

hal-02974206 , version 1 (21-10-2020)



Gilles Blanchard, Peter Mathé, Nicole Mücke. Lepskii Principle in Supervised Learning. 2020. ⟨hal-02974206⟩
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