  Machine learning models are often deployed in different settings than they
were trained and validated on, posing a challenge to practitioners who wish to
predict how well the deployed model will perform on a target distribution. If
an unlabeled sample from the target distribution is available, along with a
labeled sample from a possibly different source distribution, standard
approaches such as importance weighting can be applied to estimate performance
on the target. However, importance weighting struggles when the source and
target distributions have non-overlapping support or are high-dimensional.
Taking inspiration from fields such as epidemiology and polling, we develop
Mandoline, a new evaluation framework that mitigates these issues. Our key
insight is that practitioners may have prior knowledge about the ways in which
the distribution shifts, which we can use to better guide the importance
weighting procedure. Specifically, users write simple "slicing functions" -
noisy, potentially correlated binary functions intended to capture possible
axes of distribution shift - to compute reweighted performance estimates. We
further describe a density ratio estimation framework for the slices and show
how its estimation error scales with slice quality and dataset size. Empirical
validation on NLP and vision tasks shows that Mandoline can estimate
performance on the target distribution up to 3x more accurately compared to
standard baselines.
