Summary of Uncertainty Herding: One Active Learning Method For All Label Budgets, by Wonho Bae et al.
Uncertainty Herding: One Active Learning Method for All Label Budgets
by Wonho Bae, Gabriel L. Oliveira, Danica J. Sutherland
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This research paper proposes a novel approach to active learning, addressing the issue of existing methods performing poorly when label budgets are small or large. The proposed method, Uncertainty Herding, is a simple yet effective solution that optimizes uncertainty coverage, a objective that generalizes various low- and high-budget objectives. This method outperforms state-of-the-art performance in most cases across different active learning tasks, making it a reliable choice for both low- and high-budget settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper solves a big problem with how we learn from data when we don’t have enough labels. Right now, some methods work great when we have lots of labels, but others only do well when we have few labels. This is a problem because different problems require different amounts of labels. The researchers propose a new way to learn from data called Uncertainty Herding that works well in both situations. It’s simple and fast, and it outperforms other methods in most cases. |
Keywords
» Artificial intelligence » Active learning
