Research map / Learning theory and algorithms / Prediction and decision losses
A Coverage-Based Utility Model for Identifying Unknown Unknowns
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use classifier-confidence prior instead of uniform prior in greedy UU search (ablation) | Greedy search · Kaggle13 and three other datasets | coverage utility | Helped |
| use Greedy-conf instead of the bandit UUB of Lakkaraju et al. for coverage utility | Greedy-conf · Pang04 | % gain over UUB: 24.08 | Helped |
| use Greedy-conf instead of UUB | Greedy-conf · Kaggle13 | % gain over UUB: 46.37 | Helped |
| use Greedy-conf instead of Most-uncertain baseline | Greedy-conf · all four datasets | coverage utility | Helped |
| use bandit UUB instead of simple Most-uncertain under coverage utility | UUB · coverage utility | coverage utility | Hurt |
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