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Extrapolation-aware statistical machine learning

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If you have a question about this talk, please contact Qingyuan Zhao .

Nonparametric function estimation and prediction with moderate or large dimension of the covariates are particularly susceptible to extrapolation, because data points are typically far apart from each other in such moderate or higher dimension. Thus, there is a need to have machine learning methods that are extrapolation-aware, i.e. that automatically perform well (in a sense) when extrapolation occurs. Without such extrapolation-aware techniques, inference from standard machine learning and nonparametric procedures may be poor or invalid. We introduce a novel conceptual framework and introduce Xtrapolation which allows for extrapolation-aware inference with any ML algorithm.

This is joint work with Niklas Pfister (Lakera AI)

This talk is part of the Statistics series.

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