Research map / Learning theory and algorithms / Prediction and decision losses
Controlling the distance to a Kemeny consensus without computing it
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use a better-approximating voting rule (Borda/Copeland/QuickSort) instead of a naive one (Pick-a-Random) as the initial ranking for Method 1 | Method 1 bound on sushi dataset · sushi (N=5000, n=10) | k_min distance bound: 14 vs 35.09 for Pick-a-Random | Helped |
| apply the bound with Pick-a-Random (negative control) rather than a tractable voting rule | Method 1 · APA and sushi | tightness s(r,DN,n) and k_min: largest and widest boxplots | Hurt |
| increase alternative set size n from 3 to 5 (restricted sushi datasets) | Method 1 tightness · restricted sushi (n=3,4,5) | tightness s(r,DN,n): s increases with n | Hurt |
| dataset has a Condorcet winner vs no Condorcet winner | Method 1 tightness · Netflix (n=4) | tightness s(r,DN,n): s generally smaller with Condorcet winner | Helped |
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