Robust importance-weighted cross-validation under sample selection bias

17 Oct 2017  ·  Wouter M. Kouw, Jesse H. Krijthe, Marco Loog ·

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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