Research map / Learning theory and algorithms / Prediction and decision losses
Satisfying Real-world Goals with Dataset Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use deterministic thresholding instead of randomized classification for the learned classifier (ablation) | proposed churn-constrained classifier · Google churn/recall dataset | error rate and churn | Helped |
| Replace thresholded SVM baseline with dataset-constrained ramp-loss classifier (churn and recall constraints) | Google churn/recall dataset · Google churn/recall dataset | churn and error rate | Helped |
| Use dataset-constrained ramp-loss formulation instead of linear fairness covariance constraint of prior work | Adult fairness constraint · Adult (a9a) | test error vs fairness ratio | Helped |
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