8 citations · 26 across the 13 of their papers we have counts for
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math.ST2019★ 6 cited
Lepskii Principle in Supervised Learning
Gilles Blanchard, Peter Mathé, Nicole Mücke
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 regulari…
stat.ML2019★ 4 cited
Empirical Risk Minimization in the Interpolating Regime with Application to Neural Network Learning
Nicole Mücke, Ingo Steinwart
A common strategy to train deep neural networks (DNNs) is to use very large architectures and to train them until they (almost) achieve zero training error. Empirically observed go…