RESEARCHMonitorWATCHLIST
Estimating Performance for Safe Deployment of Machine Learning Models
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Research proposes methods for estimating pre-trained model performance on unlabeled test data subject to distribution drift.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 22 October 2026
- Collected by OneBench
- 23 Oct 2026, 03:02 UK
Stored source excerpt
arXiv:2610.29262v1 Announce Type: new Abstract: The main challenge in applying deep neural networks to real-world scenarios lies in distributional discrepancies between training and test datasets,…
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