arxiv
PublishedJune 4, 2026 at 4:00 AM
—neutral
Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning
Publisher summary· verbatim
arXiv:2605.23595v2 Announce Type: replace-cross Abstract: The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released models on unseen and unlabeled data. Existing evaluation pipelin
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivSFMambaNet: Spectral-Frequency Enhanced Selective State Space Model for Correspondence Pruning15harxivOptical-Guided Neural Collapse for SAR Few-Shot Class Incremental Learning15harxivDynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models15hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗