Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed semiparametric TS algorithm instead of stationary-model TS | Contextual bandit · Simulation, N=2, ν(t)=-b_{a*(t)}(t)^Tμ | median cumulative regret: 59 -> 17 | Helped |
| Use the proposed semiparametric TS algorithm instead of stationary-model TS | Contextual bandit · Simulation, N=2, ν(t)=log(t+1) | median cumulative regret: 1020 -> 629 | Helped |
| Use the proposed semiparametric TS algorithm instead of stationary-model TS | Contextual bandit · Simulation, N=6, ν(t)=-b_{a*(t)}(t)^Tμ | median cumulative regret: 70 -> 29 | Helped |
| Use the proposed semiparametric TS algorithm instead of stationary-model TS | Contextual bandit · Simulation, N=6, ν(t)=log(t+1) | median cumulative regret: 9026 -> 1186 | Helped |
| Use the proposed semiparametric TS algorithm instead of stationary-model TS | Contextual bandit · Simulation, N=2, ν(t)=0 | median cumulative regret: 2 -> 20 | Hurt |
| Use the proposed semiparametric TS algorithm instead of ACTS | Contextual bandit · Simulation, N=6, ν(t)=log(t+1) | median cumulative regret: 3772 -> 1186 | Helped |
| Use the proposed semiparametric TS algorithm instead of TS | Yahoo! R6A policy evaluation · Yahoo! R6A, May 2–10, 2009 | mean user clicks: 86907.0 -> 90689.7; 4.4% | Helped |
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