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Does data interpolation contradict statistical optimality?

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arxiv 1806.09471 v1 pith:HKECSENN submitted 2018-06-25 stat.ML cs.LGmath.STstat.TH

Mikhail Belkin , Alexander Rakhlin , Alexandre B. Tsybakov This is my paper · ORCID
classification stat.MLcs.LGmath.STstat.TH
keywords dataachievecontradictinterpolatinginterpolationlearninglossmethods
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We show that learning methods interpolating the training data can achieve optimal rates for the problems of nonparametric regression and prediction with square loss.

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  1. On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels

    math.ST 2019-08 conditional novelty 8.0 of 10

    Minimum-norm interpolants in reproducing kernel Hilbert spaces have risk that can exhibit multiple peaks and valleys as the sample size grows, with peak locations predicted by the scaling d = n^α.

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