  Many applications utilize sensors in mobile devices and machine learning to
provide novel services. However, various factors such as different users,
devices, and environments impact the performance of such applications, thus
making the domain shift (i.e., distributional shift between the training domain
and the target domain) a critical issue in mobile sensing. Despite attempts in
domain adaptation to solve this challenging problem, their performance is
unreliable due to the complex interplay among diverse factors. In principle,
the performance uncertainty can be identified and redeemed by performance
validation with ground-truth labels. However, it is infeasible for every user
to collect high-quality, sufficient labeled data. To address the issue, we
present DAPPER (Domain AdaPtation Performance EstimatoR) that estimates the
adaptation performance in a target domain with only unlabeled target data. Our
key idea is to approximate the model performance based on the mutual
information between the model inputs and corresponding outputs. Our evaluation
with four real-world sensing datasets compared against six baselines shows that
on average, DAPPER outperforms the state-of-the-art baseline by 39.8% in
estimation accuracy. Moreover, our on-device experiment shows that DAPPER
achieves up to 396X less computation overhead compared with the baselines.
