On $f$-Divergence Principled Domain Adaptation: An Improved Framework

Ziqiao Wang; Yongyi Mao

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arXiv · 2402.01887

On $f$-Divergence Principled Domain Adaptation: An Improved Framework

Ziqiao Wang·Yongyi Mao

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Abstract

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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BibTeX RIS

Ziqiao Wang, Yongyi Mao. 2024-10-27. On $f$-Divergence Principled Domain Adaptation: An Improved Framework. https://arxiv.org/abs/2402.01887

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