Papers › On f-Divergence Principled Domain Adaptation: An Improved Framework

On f-Divergence Principled Domain Adaptation: An Improved Framework

2 Feb 2024arXiv:2402.01887archive 2025-07-28

Ziqiao Wang, Yongyi Mao

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 in 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 obtains 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). Using a localization technique, we also develop a fast-rate generalization bound. Empirical results demonstrate the superior performance of f-DD-based learning algorithms over previous works in popular UDA benchmarks.

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