arXiv Machine Learning July 1, 2026 By Wooseok Ha, Yuansi Chen

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

Read the original on arXiv Machine Learning →

arXiv:2507. 14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiting abundant source and unlabeled target data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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