Research map / Learning theory and algorithms / Prediction and decision losses
Optimizing Positional Scoring Rules for Rank Aggregation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize the positional scoring vector for the given constraints instead of using a fixed rule | OptPSR Example (d=4) · OptPSR Example (d=4) | gain: 7 -> 10 | Helped |
| Search over all approval rules and choose the highest-gain rule | OptPSR instances · OptPSR instances | approximation ratio: 1/d | Helped |
| Search scoring vectors with a k-pattern rather than only approval vectors | OptPSR instances · OptPSR instances | approximation ratio: ⌈d/k⌉^{-1} | Helped |
| Use the Regions algorithm to optimize exactly over valid scoring vectors | OptPSR instances · OptPSR instances | optimality | Helped |
| Use well-known scoring rules or BestApproval and ApxPSR on the tested real-world and synthetic profiles | Scoring rules and algorithms · ppl and col scenarios; real-world and synthetic profiles | desired constraints recovered: almost 100% | Helped |
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