Summary of Uncertainty For Active Learning on Graphs, by Dominik Fuchsgruber et al.
Uncertainty for Active Learning on Graphs
by Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier, Antonio Oroz, Stephan Günnemann
First submitted to arxiv on: 2 May 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 proposed study explores Uncertainty Sampling as an Active Learning strategy for node classification on graphs. The strategy iteratively acquires labels for data points with high uncertainty, aiming to improve model efficiency. The authors benchmark Uncertainty Sampling against other strategies and highlight a significant performance gap. They also develop Bayesian uncertainty estimates grounded in the data generating process and demonstrate their effectiveness in guiding queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Uncertainty Sampling is a way to help machine learning models learn faster by asking for labels on the most uncertain data points. Researchers have used this method before, but it hasn’t been tested much on graph-like data. In this study, scientists explored Uncertainty Sampling for node classification on graphs and found that it can be very effective when combined with special uncertainty estimates. They also compared their approach to others and showed how some common methods have limitations. |
Keywords
» Artificial intelligence » Active learning » Classification » Machine learning
