Research map / Bandits and online learning / Online convex optimization and games
Weighted Voting Via No-Regret Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use randomized single-voter weights updated from full-information losses | Any anonymous voting rule · Full information | regret: O(\sqrt{T \ln(n)}) | Helped |
| Use randomized single-voter weights with importance-weighted updates from observed winner loss | Any anonymous voting rule · Partial information | regret: O(\sqrt{T n \ln(n)}) | Helped |
| Keep the weights deterministic and use a voting rule that is a distribution over unilaterals | Full-information weighting scheme · Full information; distributions over unilaterals | regret: O(\sqrt{T \ln(n)}) | Helped |
| Keep the weights deterministic and use a voting rule that is a distribution over unilaterals | Partial-information weighting scheme · Partial information; distributions over unilaterals | regret: O(\sqrt{T n \ln(n)}) | Helped |
| Use a deterministic weighting scheme with a deterministic rule that is not constant on unanimous profiles | Deterministic voting rule · Full information and partial information | regret: \Omega(T) | Hurt |
| Use a deterministic weighting scheme with a probabilistically Condorcet-consistent rule | Randomized voting rule · Full information | regret: \Omega(T) | Hurt |
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