A distributionally robust training objective with likelihood-ratio pseudo-labels improves classification accuracy under noisy crowd labels.
Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
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Learning from Noisy Labels via Conditional Distributionally Robust Optimization
A distributionally robust training objective with likelihood-ratio pseudo-labels improves classification accuracy under noisy crowd labels.