{
  "id": 18322,
  "url": "https://arxiv.org/abs/2608.09193v1",
  "title": "CPDA: Class-Conditional Path Distribution Alignment for Unsupervised Time-Series Domain Adaptation",
  "summary": "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",
  "authors": "Felix Ott, Christopher Mutschler",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T07:05:58.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18322",
  "original_url": "https://arxiv.org/abs/2608.09193v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}