Summary of Robust Offline Active Learning on Graphs, by Yuanchen Wu and Yubai Yuan
Robust Offline Active Learning on Graphs
by Yuanchen Wu, Yubai Yuan
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 A novel offline active learning approach is proposed for selecting nodes to query in graph-structured networks where labeling node responses is costly. The method incorporates information from both network structure and node covariates using a two-stage biased sampling strategy that balances informativeness (complexity of learnable graph signals) and representativeness (generalization error control). Theoretical results demonstrate the trade-off between these factors, while experiments show competitive performance with existing methods, particularly when node responses contain noise. This approach is applicable to both regression and classification tasks on graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to explore a new way of choosing which nodes to ask for information in big networks where labeling takes a lot of time. Instead of just using the network’s structure, we’ll also look at what we know about each node. This helps us find the right balance between how much we can learn from asking certain nodes and how well our answers will generalize to other parts of the network. The new method works for both simple and complex tasks on these networks. |
Keywords
» Artificial intelligence » Active learning » Classification » Generalization » Regression
