Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning 事件

Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning 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 pipelines typically rely on costly annotation, repeated fine-tuning, or assumptions that do not generalize well to