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.