An unlabeled test-time adaptation method updates concept vectors, the linear predictor, and adds residual concepts so that concept bottleneck classifiers stay accurate under distribution shifts.
Meaningfully debugging model mistakes using conceptual counterfactual explanations
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Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts
An unlabeled test-time adaptation method updates concept vectors, the linear predictor, and adds residual concepts so that concept bottleneck classifiers stay accurate under distribution shifts.