Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace estimates for arms with large confidence intervals by B | C²UCB on grouped-type constraints · Grouped type | mean regret | Helped |
| Use ordinary C²UCB optimistic estimates rather than capping large-confidence arms at B | CCS with general constraints · Grouped type | mean regret | Hurt |
| Replace estimates for arms with large confidence intervals by B | C²UCB on partition matroid constraints · Uniform matroid type | mean regret | No clear effect |
| Use C²UCB with partition matroid constraints rather than general constraints | C²UCB · CCS | regret upper bound: \tilde{O}\left(Rd\sqrt{kT} + \min\left(LS, Bk\right)dk\right) -> \tilde{O}\left( Rd\sqrt{kT} + Bdk \right) | Helped |
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