A new research paper explores the number of labels required for model selection, particularly focusing on the area under the generalized risk-coverage curve (AUGRC). The study introduces a prelabel lower bound to identify insufficient label budgets and a covering linear program to determine the minimum labels needed to definitively select a winning model. Results from comparisons on nine datasets show that disagreement labels can resolve accuracy choices but not AUGRC choices, with an average certificate size of 56-57% of labels. Further experiments with pretrained image classifiers indicate that confidence-score selection requires a significant portion of labels, even with AUGRC tolerance. AI
IMPACT Provides a theoretical framework and empirical evidence for optimizing data labeling strategies in machine learning.
RANK_REASON Research paper published on arXiv detailing a new methodology for model selection. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- AUGRC
- Bernoulli
- Hugging Face
- machine learning
- ScienceCast
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