  Deploying machine learning models to new tasks is a major challenge despite
the large size of the modern training datasets. However, it is conceivable that
the training data can be reweighted to be more representative of the new
(target) task. We consider the problem of reweighing the training samples to
gain insights into the distribution of the target task. Specifically, we
formulate a distribution shift model based on the exponential tilt assumption
and learn train data importance weights minimizing the KL divergence between
labeled train and unlabeled target datasets. The learned train data weights can
then be used for downstream tasks such as target performance evaluation,
fine-tuning, and model selection. We demonstrate the efficacy of our method on
Waterbirds and Breeds benchmarks.
