Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Linear Submodular Bandits with a Knapsack Constraint
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use MCSGreedy or CGreedy (unit-cost UCB greedy) instead of LSBGreedy and Epsilon-Greedy | linear submodular bandit with knapsack constraint · simulated news recommendation | average rewards | Helped |
| use MCSGreedy or CGreedy instead of baselines under limited budget | linear submodular bandit with knapsack constraint · simulated news recommendation, varying budget | average rewards | Helped |
| use MCSGreedy instead of CGreedy for parameter estimation | linear submodular bandit with knapsack constraint · simulated news recommendation | ||w* - wt|| | Helped |
| use MCSGreedy or CGreedy with large cost intervals | linear submodular bandit with knapsack constraint · simulated news recommendation, varying cost interval | average rewards | Helped |
| sample arm costs from a Gaussian instead of uniform distribution | MCSGreedy and CGreedy · simulated news recommendation | average rewards | No clear effect |
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