"When deployment conditions stop resembling the past, the problem is distribution shift, with out-of-distribution inputs being one important part of it."
Wang and colleagues distinguish different forms of distribution shift, including semantic and covariate changes. Their research establishes the technical foundation for the section's argument that deployment conditions can depart from the world represented in a model's training data.
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