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BLOOD: Bi-level Learning Framework for Out-of-distribution Generalization
In this paper, we propose a novel bi-level learning framework for OOD generalization, which can effectively remove multiple unknown types of biases without any prior bias information or separate re-training steps of a model.
Meta-Learned Invariant Risk Minimization
Our method is more robust to the data with spurious correlations and can provide an invariant optimal classifier even when data from each distribution are scarce.
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