Summary of Few-shot Causal Representation Learning For Out-of-distribution Generalization on Heterogeneous Graphs, by Pengfei Ding and Yan Wang and Guanfeng Liu and Nan Wang and Xiaofang Zhou
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
by Pengfei Ding, Yan Wang, Guanfeng Liu, Nan Wang, Xiaofang Zhou
First submitted to arxiv on: 7 Jan 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 proposed Causal OOD Heterogeneous graph Few-shot learning model, COHF, addresses the problem of out-of-distribution generalization in heterogeneous graphs by characterizing distribution shifts with a structural causal model and developing a variational autoencoder-based neural network to mitigate their impact. By integrating this network with a meta-learning framework, COHF effectively transfers knowledge from rich-labeled classes in a source graph to facilitate learning new classes with few-labeled training data. The model achieves superior performance over state-of-the-art methods on seven real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary COHF is a new way for machines to learn from graphs that are different from the ones they learned from before. This helps when we have very little information about what we’re trying to predict. The idea is to take knowledge from similar places and apply it to new, unfamiliar situations. COHF does this by looking at how things are connected in a graph, and then using that understanding to make better predictions. |
Keywords
* Artificial intelligence * Few shot * Generalization * Meta learning * Neural network * Variational autoencoder
