Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong Park. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Simon A. Aytes, Jinheon Baek, Sung Ju Hwang. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Soyeong Jeong, Kangsan Kim, Jinheon Baek, Sung Ju Hwang. Findings of the Association for Computational Linguistics: ACL 2025. 2025.

Jaewoo Lee, Boyang Li, Sung Ju Hwang. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

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.