@inproceedings{da88aa2f659e44e9a9c39c1c625afdd7,
title = "Robust Importance-Weighted Cross-Validation under Sample Selection Bias",
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 suboptimal 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.",
keywords = "cross-validation, Sample selection bias",
author = "Kouw, {Wouter M.} and Krijthe, {Jesse H.} and Marco Loog",
year = "2019",
doi = "10.1109/MLSP.2019.8918731",
language = "English",
isbn = "978-1-7281-0825-4",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE ",
pages = "1--6",
booktitle = "2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019",
address = "United States",
note = "29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 ; Conference date: 13-10-2019 Through 16-10-2019",
}