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Podcast: Arxiv Papers
Episode: OOD-CHAMELEON: Is Algorithm Selection for OOD Generalization Learnable?
Description:
The paper introduces OOD-CHAMELEON, a method for selecting algorithms for out-of-distribution generalization by predicting performance based on dataset characteristics, outperforming individual algorithms and heuristics.
https://arxiv.org/abs//2410.02735
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