On the robustness of kernel-based pairwise learning

Patrick Gensler; Andreas Christmann

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arXiv · 2010.15527

On the robustness of kernel-based pairwise learning

Patrick Gensler·Andreas Christmann

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Abstract

It is shown that many results on the statistical robustness of kernel-based pairwise learning can be derived under basically no assumptions on the input and output spaces. In particular neither moment conditions on the conditional distribution of Y given X = x nor the boundedness of the output space is needed. We obtain results on the existence and boundedness of the influence function and show qualitative robustness of the kernel-based estimator. The present paper generalizes results by Christmann and Zhou (2016) by allowing the prediction function to take two arguments and can thus be applied in a variety of situations such as ranking.

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Patrick Gensler, Andreas Christmann. 2020-10-29. On the robustness of kernel-based pairwise learning. https://arxiv.org/abs/2010.15527

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