Robust importance-weighted cross-validation under sample selection bias

Wouter M. Kouw; Jesse H. Krijthe; Marco Loog

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arXiv · 1710.06514

Robust importance-weighted cross-validation under sample selection bias

Wouter M. Kouw·Jesse H. Krijthe·Marco Loog

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Abstract

Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk. However, the importance-weighted risk estimator produces sub-optimal hyperparameter estimates in problem settings where large weights arise with high probability. We study its sampling variance as a function of the training data distribution and introduce a control variate to increase its robustness to problematically large weights.

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Wouter M. Kouw, Jesse H. Krijthe, Marco Loog. 2017-10-17. Robust importance-weighted cross-validation under sample selection bias. https://arxiv.org/abs/1710.06514

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