Research map / Learning theory and algorithms / Submodular and game algorithms
Effective Heuristics for Committee Scoring Rules
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use proposed OWA schedule instead of 0-appending schedule in removal algorithm (ablation) | Removal heuristic · CC, 2D uniform square, k=10 | average error vs optimal: 10% -> 3% | Helped |
| use Banzhaf or removal heuristic instead of greedy | committee scoring rule heuristics · t-Borda, IC and 2D, k=10 | approximation ratio: error never exceeds 10% | Helped |
| use Banzhaf instead of removal heuristic | t-Borda heuristics · IC model | approximation ratio | Helped |
| use Banzhaf instead of removal heuristic | t-Borda heuristics · 2D uniform square | approximation ratio | Hurt |
| use removal or Banzhaf instead of simulated annealing | CC rule heuristics · CC, varying committee size | approximation ratio | Mixed |
| use removal/Banzhaf instead of simulated annealing | heuristics · t-Borda k=10 | running time: 1s -> 10s (removal), 24s (Banzhaf) | Hurt |
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