Summary of Conditional Shift-robust Conformal Prediction For Graph Neural Network, by S. Akansha
Conditional Shift-Robust Conformal Prediction for Graph Neural Network
by S. Akansha
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach to quantify uncertainty in Graph Neural Network (GNN) predictions by leveraging conformal prediction. This addresses limitations of traditional GNNs, which struggle to provide robust uncertainty estimates. The method, called Conditional Shift Robust (CondSR), is model-agnostic and adaptable to various classification models. It aims to refine GNN predictions by minimizing conditional shift in latent stages. The approach is validated on standard graph benchmark datasets, achieving up to 12% higher accuracy than state-of-the-art GNNs under conditional shift. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a way to make Graph Neural Networks more reliable by giving them better uncertainty estimates. This is important because these networks are good at making predictions, but they don’t always know how sure they are of those predictions. The new approach uses something called conformal prediction and it helps the network give more accurate and robust results. It’s tested on some standard datasets and shows that it can improve the accuracy of GNNs by up to 12%. |
Keywords
» Artificial intelligence » Classification » Gnn » Graph neural network
