Summary of Neural Active Learning Beyond Bandits, by Yikun Ban et al.
Neural Active Learning Beyond Bandits
by Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu, Kommy Weldemariam, Hanghang Tong, Jingrui He
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates active learning strategies with neural network approximations, building upon recent bandit-based approaches that transformed active learning into a decision-making problem. The authors seek to mitigate the adverse impacts of the number of classes (K) on performance and computational costs while retaining the benefits of principled exploration and provable guarantees in active learning. To achieve this, they propose two algorithms for stream-based and pool-based active learning, respectively, based on exploitation and exploration neural networks. Theoretical performance guarantees are provided in a non-parametric setting, showing that the proposed approaches exhibit slower error-growth rates with respect to K. Experimental results demonstrate consistent outperformance of state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to make active learning more efficient by using special kinds of neural networks. Active learning is a way to improve machine learning models without needing as much data. The researchers want to know how to avoid problems that happen when you have many classes (or categories) in your data. They come up with two new algorithms for stream-based and pool-based active learning, which they test against other popular approaches. Their results show that these new methods are better at handling big datasets. |
Keywords
» Artificial intelligence » Active learning » Machine learning » Neural network
