Detecting and Correcting for Label Shift with Black Box Predictors¶
Lipton, Z. C., Wang, Yu-Xiang, & Smola, A. J. (2018). Detecting and Correcting for Label Shift with Black Box Predictors.
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1 citation across 1 artifact.
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Domain-specific¶
- Label Shift
- The estimators for this correction — Black Box Shift Estimation (BBSE, Lipton et al. 2018), regularised learning under label shift (RLLS, Azizzadenesheli et al. 2019), and maximum-likelihood label-shift estimation (MLLS) — recover the deployment P(Y) from a small unlabelled deployment sample using the model's own confusion matrix from training, then reweight predicted probabilities and recalibrate operating thresholds to the deployment prior
This sourceBlack Box Shift Estimation, which recovers the shifted label marginal from unlabelled deployment data via the predictor's expected confusion matrix and corrects the classifier by importance weighting.
Supported in partVerified against the source
- The estimators for this correction — Black Box Shift Estimation (BBSE, Lipton et al. 2018), regularised learning under label shift (RLLS, Azizzadenesheli et al. 2019), and maximum-likelihood label-shift estimation (MLLS) — recover the deployment P(Y) from a small unlabelled deployment sample using the model's own confusion matrix from training, then reweight predicted probabilities and recalibrate operating thresholds to the deployment prior
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