Paper ID: 2402.01887 • Published Feb 2, 2024
On f-Divergence Principled Domain Adaptation: An Improved Framework
Ziqiao Wang, Yongyi Mao
TL;DR
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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.