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Yuhao Tang

2 accepted papers

2025

Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation

ICML 2025poster

Unsupervised Graph Domain Adaptation (UGDA) leverages labeled source domain graphs to achieve effective performance in unlabeled target domains despite distribution shifts. However, existing methods often yield suboptimal results due to the entanglement of causal-spurious features and the failure of…

Cited by 0SourcePDFScholar
2025

Two-Stage Feature Generation with Transformer and Reinforcement Learning

IJCAI 2025

Feature generation is a critical step in machine learning, aiming to enhance model performance by capturing complex relationships within the data and generating meaningful new features. Traditional feature generation methods heavily rely on domain expertise and manual intervention, making the proces

Cited by 0SourcePDFScholar