DeCaf is a causal decoupling framework that independently learns unbiased feature-label and structure-label mappings to mitigate the impact of distribution shifts in Graph Neural Networks.

Authors

• Person: Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche Organization: AISTATS
  Track: Poster Session 3 - aistats 2026

Sources

• Track: Poster Session 3 - aistats 2026 virtual.aistats.org Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche · AISTATS via serper

Referenced by nodes (1)

• graph neural networks concept