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Deep-Spurious-Regression

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  [![Paper](https://img.shields.io/badge/paper-arXiv-red?logo=arxiv)](https://arxiv.org/abs/XXXX.XXXXX)
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  [![Webpage](https://img.shields.io/badge/%F0%9F%8C%90%20website-demo-blue)](https://yang-ai-lab.github.io/Deep-Spurious-Regression/)
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  [![License](https://img.shields.io/badge/%F0%9F%93%9C%20license-MIT-green)](LICENSE)
 
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  Real-world regression often exhibits shortcuts: attributes spuriously correlated with continuous targets during training that become unreliable under deployment shifts. Existing work on spurious correlations focuses primarily on classification, where labels are categorical and groups are naturally defined. However, many real-world tasks require **continuous prediction**, where hard label boundaries or discrete group-label pairs do not exist.
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  [![Paper](https://img.shields.io/badge/paper-arXiv-red?logo=arxiv)](https://arxiv.org/abs/XXXX.XXXXX)
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  [![Webpage](https://img.shields.io/badge/%F0%9F%8C%90%20website-demo-blue)](https://yang-ai-lab.github.io/Deep-Spurious-Regression/)
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  [![License](https://img.shields.io/badge/%F0%9F%93%9C%20license-MIT-green)](LICENSE)
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+ [![Python](https://img.shields.io/badge/python-3.9%2B-brightgreen)](#installation)
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  Real-world regression often exhibits shortcuts: attributes spuriously correlated with continuous targets during training that become unreliable under deployment shifts. Existing work on spurious correlations focuses primarily on classification, where labels are categorical and groups are naturally defined. However, many real-world tasks require **continuous prediction**, where hard label boundaries or discrete group-label pairs do not exist.
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