Research map / Label-efficient and robust learning / Recognition and long-tail
Covariate-Shift Generalization via Random Sample Weighting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use random covariate-dependent sample weights to create environments for invariant learning | RSW · Synthetic covariate-shift simulations | Mean and worst-case MSE | Helped |
| Use random sample weighting rather than ERM | Linear prediction under Simulation 1, r = 2 · Simulation 1, r = 2 | Mean MSE: 0.5768 -> 0.4175 | Helped |
| Use random sample weighting rather than ERM | Linear prediction under Simulation 1, r = 2 · Simulation 1, r = 2 | Worst-case MSE: 1.1525 -> 0.6226 | Helped |
| Increase the number of random sample weightings at each optimization step (ablation) | RSW · People Income Prediction | Accuracy | No clear effect |
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