On f-Divergence Principled Domain Adaptation: An Improved Framework
<|reference_start|>On f-Divergence Principled Domain Adaptation: An Improved Framework: Unsupervised domain adaptation (UDA) plays a crucial role in addressing distribution shifts in machine learning. In this work, we improve the theoretical foundations of UDA proposed by Acuna et al. (2021) by refining their f-divergence-based discrepancy and additionally introducing a new measure, f-domain discrepancy (f-DD). By removing the absolute value function and incorporating a scaling parameter, f-DD yields novel target error and sample complexity bounds, allowing us to recover previous KL-based results and bridging the gap between algorithms and theory presented in Acuna et al. (2021). Leveraging a localization technique, we also develop a fast-rate generalization bound. Empirical results demonstrate the superior performance of f-DD-based domain learning algorithms over previous works in popular UDA benchmarks.<|reference_end|>
@article{wang2024on,
    title={On $f$-Divergence Principled Domain Adaptation: An Improved Framework},
    author={Ziqiao Wang, Yongyi Mao},
    journal={arXiv preprint arXiv:2402.01887},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01887},
    primaryClass={stat.ML cs.CV cs.LG}
}