<ul><li>Out-of-distribution (OOD) generalization is a challenge in machine learning, where models trained on one data distribution often perform poorly on shifted domains.</li><li>A study focuses on designing training data distributions to enhance average-case OOD performance.</li><li>The research introduces algorithmic strategies to minimize OOD error, such as bilevel optimization and theoretical upper bound minimization.</li><li>Experimental evaluation shows significant improvements in OOD accuracy compared to standard empirical risk minimization, emphasizing the importance of distribution-aware training for robust OOD generalization.</li></ul>

Learning Where to Learn: Training Distribution Selection for Provable OOD Performance

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