Summary of Conformal Load Prediction with Transductive Graph Autoencoders, by Rui Luo and Nicolo Colombo
Conformal Load Prediction with Transductive Graph Autoencoders
by Rui Luo, Nicolo Colombo
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Graph Neural Network (GNN) approach for predicting edge weights on graphs, with guaranteed coverage. The GNN is calibrated using conformal prediction to produce valid prediction intervals. To handle data heteroscedasticity, the authors use error reweighting and Conformalized Quantile Regression (CQR). The method is evaluated on real-world transportation datasets, showing better coverage and efficiency compared to baseline techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict how important different paths are in complex networks like traffic systems or social media. It uses special computer programs called Graph Neural Networks to make these predictions. To make sure the predictions are reliable, it also figures out ways to handle when the data is not equally good all around. The results show that this approach works better than other methods on real-world transportation datasets. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Regression
