Active Learning under Label Shift
This addresses active learning challenges in domain adaptation for scenarios with shifting class distributions, though it is incremental as it builds on existing active learning and label shift methods.
The paper tackles active learning under label shift, where class proportions differ between source and target domains, by introducing a method called MALLS that balances bias and variance, reducing sample complexity by 60% in deep active learning tasks.
We address the problem of active learning under label shift: when the class proportions of source and target domains differ. We introduce a "medial distribution" to incorporate a tradeoff between importance weighting and class-balanced sampling and propose their combined usage in active learning. Our method is known as Mediated Active Learning under Label Shift (MALLS). It balances the bias from class-balanced sampling and the variance from importance weighting. We prove sample complexity and generalization guarantees for MALLS which show active learning reduces asymptotic sample complexity even under arbitrary label shift. We empirically demonstrate MALLS scales to high-dimensional datasets and can reduce the sample complexity of active learning by 60% in deep active learning tasks.