Research map / Learning theory and algorithms / Prediction and decision losses
Learning Distributionally Robust Models at Scale via Composite Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GCIVR instead of the Heavily-constrained method with a linear multiplier model | Adult equality-of-opportunity fairness · Adult | overall error rate | Helped |
| Use GCIVR instead of the Heavily-constrained method with a linear multiplier model | Adult equality-of-opportunity fairness · Adult | maximum fairness-constraint violation | No clear effect |
| Use GCIVR instead of unconstrained optimization | Adult equality-of-opportunity fairness · Adult | runtime | Helped |
| Use GCIVR instead of the Heavily-constrained method with a two-layer neural multiplier | Intersectional-group fairness · Communities and Crime | maximum fairness-constraint violation | Helped |
| Use GCIVR instead of the Heavily-constrained method with a two-layer neural multiplier | Intersectional-group fairness · Communities and Crime | error rate | No clear effect |
| Use GCIVR instead of the Heavily-constrained method with a two-layer neural multiplier | Intersectional-group fairness · Communities and Crime | runtime: >100 times | Helped |
| Use GCIVR instead of the Heavily-constrained method with a one-layer neural multiplier | Per-query ranking fairness · MSLR-WEB10K | error rate | Helped |
| Use GCIVR instead of the Heavily-constrained method with a one-layer neural multiplier | Per-query ranking fairness · MSLR-WEB10K | maximum group-constraint violation | Helped |
| Use GCIVR instead of the Heavily-constrained method with a one-layer neural multiplier | Per-query ranking fairness · MSLR-WEB10K | runtime: 2× | Helped |
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