Summary of Malady: Multiclass Active Learning with Auction Dynamics on Graphs, by Gokul Bhusal et al.
MALADY: Multiclass Active Learning with Auction Dynamics on Graphs
by Gokul Bhusal, Kevin Miller, Ekaterina Merkurjev
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT); Optimization and Control (math.OC)
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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 introduces a new framework called Multiclass Active Learning with Auction Dynamics on Graphs (MALADY), which uses auction dynamics to efficiently select unlabeled data points for labeling in semi-supervised learning. The algorithm generalizes previous work on similarity graphs and incorporates a novel active learning acquisition function that prioritizes queries near decision boundaries between classes. Experiments show that MALADY outperforms comparison algorithms in classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to help machines learn from limited labeled data by choosing which unlabeled data points to label. It uses an algorithm called auction dynamics, which is usually used for something else. The researchers made this algorithm work better for semi-supervised learning and added a new way to decide what data point to label next. This approach does better than others in some experiments. |
Keywords
» Artificial intelligence » Active learning » Classification » Semi supervised
