Research map / Learning theory and algorithms / Prediction and decision losses
You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add isotonic projection using the owner's true ranking | Reviewer scores · True-ranking isotonic estimation | expected squared error | Helped |
| Use isotonic projection with the true ranking | Reviewer scores with independent normal noise · Normal-noise isotonic estimation | worst-case expected squared error: 0.4096 + o_n(1) ≤ [risk] / [n^{1/3} σ^{4/3} V^{2/3}] ≤ 7.5625 + o_n(1) | Helped |
| Report the true ranking rather than a different ranking | Isotonic Mechanism · Owner's expected utility | expected utility | Helped |
| Use the more faithful ranking when two rankings are ordered by majorization | Isotonic Mechanism · Owner's expected utility | expected utility | Helped |
| Report the true ordered partition using block constraints | Block Isotonic Mechanism · Partially known block ranking | expected utility | Helped |
| Mix the isotonic estimate with raw scores using a convex combination | Isotonic Mechanism · Convex-combination score | expected squared error | Helped |
| Let the monotonicity penalty coefficient tend to infinity | Penalty mechanism · Penalized isotonic mechanism | recovery of Isotonic Mechanism: λ → ∞ | Helped |
| Use the penalty mechanism with two items | Penalty mechanism · n = 2 | truthfulness | Helped |
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