Covariate Shift Adaptation by Importance Weighted Cross Validation.¶
Sugiyama, M., Krauledat, M., & Müller, K. (2007). Covariate Shift Adaptation by Importance Weighted Cross Validation. Journal of Machine Learning Research, 8, 985-1005.
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Primes¶
- Training Serving Skew
- The remedy menu is exactly the prime's four-fold structure made quantitative — re-weight training points by the importance ratio \(P_{\text{serve}}(x)/P_{\text{train}}(x)\) to move preparation toward deployment; restrict serving to the region where \(P_{\text{train}}\) had support to move deployment toward preparation; monitor a population-stability or distribution-distance statistic and retrain when it crosses a threshold; or train with regularisation and augmentation for robustness to the gap.
This sourceDevelops importance-weighting by the train/serve density ratio as the remedy for covariate shift.
- The remedy menu is exactly the prime's four-fold structure made quantitative — re-weight training points by the importance ratio \(P_{\text{serve}}(x)/P_{\text{train}}(x)\) to move preparation toward deployment; restrict serving to the region where \(P_{\text{train}}\) had support to move deployment toward preparation; monitor a population-stability or distribution-distance statistic and retrain when it crosses a threshold; or train with regularisation and augmentation for robustness to the gap.
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