Summary of Direct Acquisition Optimization For Low-budget Active Learning, by Zhuokai Zhao et al.
Direct Acquisition Optimization for Low-Budget Active Learning
by Zhuokai Zhao, Yibo Jiang, Yuxin Chen
First submitted to arxiv on: 8 Feb 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 The paper presents a novel Active Learning (AL) algorithm called Direct Acquisition Optimization (DAO), which optimizes sample selection based on expected true loss reduction to improve performance in low-budget settings. Existing AL algorithms are shown to degrade significantly when the labeling budget is low, but DAO outperforms state-of-the-art approaches across seven benchmarks. The algorithm uses influence functions to update model parameters and incorporates an additional acquisition strategy to mitigate bias in loss estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new algorithm helps machine learning models work better with limited labeled data by choosing which samples to label based on how much they will help improve the model’s performance. This is especially important when there isn’t a lot of money or resources available to label more data. |
Keywords
* Artificial intelligence * Active learning * Machine learning * Optimization
