CPDA: Class-Conditional Path Distribution Alignment for Unsupervised Time-Series Domain Adaptation
Unsupervised time-series domain adaptation (DA) addresses the challenge of transferring a classifier from a labeled source domain to an unlabeled target domain under distribution shifts induced by different users, sensors, devices, acquisition conditions, or temporal dynamics. Existing methods typically mitigate this shift by aligning marginal feature distributions through adversarial training, optimal transport, or moment-based discrepancies. In this paper, we propose Class-Conditional Path Dis
Record details
Published: 10 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment
Retrieved: 11 August 2026
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ethics.ai (10 August 2026), “CPDA: Class-Conditional Path Distribution Alignment for Unsupervised Time-Series Domain Adaptation,” evidence record 18322, https://ethics.ai/record/18322 (originally published by arXiv cs.LG).
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