Summary of All Models Are Wrong, Some Are Useful: Model Selection with Limited Labels, by Patrik Okanovic et al.
All models are wrong, some are useful: Model Selection with Limited Labels
by Patrik Okanovic, Andreas Kirsch, Jannes Kasper, Torsten Hoefler, Andreas Krause, Nezihe Merve Gürel
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for label-efficient selection of pretrained classifiers is introduced, dubbed MODEL SELECTOR. This framework efficiently identifies the best-suited model for deployment on a target dataset by sampling a small subset of highly informative examples from the unlabeled data. By leveraging extensive experiments across 18 model collections and 16 datasets, it is demonstrated that MODEL SELECTOR can drastically reduce labeling costs while consistently selecting the top-performing model. The results highlight the framework’s robustness in model selection, achieving up to 94.15% cost reduction for the best model and 72.41% for near-best models within a 1% accuracy margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MODEL SELECTOR is a new way to choose the right AI model for a job without needing as many labeled examples. It takes some easy-to-label data, picks the most important parts, and uses them to pick the best pre-trained model for the task. This helps reduce the need for labeled data by up to 94% while still getting good results. |
