Importance-weighting is a popular and well-researched technique for dealing with sample selection bias and covariate shift. It has desirable characteristics such as unbiasedness, consistency and low computational complexity. However, weighting can have a detrimental effect on an estimator as well. In this work, we empirically show that the sampling distribution of an importance-weighted estimator can be skewed. For sample selection bias settings, and for small sample sizes, the importance-weighted risk estimator produces overestimates for data sets in the body of the sampling distribution, i.e. the majority of cases, and large underestimates for data sets in the tail of the sampling distribution. These over- and underestimates of the risk lead to sub-optimal regularization parameters when used for importance-weighted validation.
|Title of host publication||2018 24th International Conference on Pattern Recognition (ICPR)|
|Number of pages||6|
|Publication status||Published - 2018|
|Event||2018 24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China|
Duration: 20 Aug 2018 → 24 Aug 2018
|Conference||2018 24th International Conference on Pattern Recognition, ICPR 2018|
|Period||20/08/18 → 24/08/18|