Research map / Bandits and online learning / Contextual bandits and Thompson sampling
An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LinUCB reward estimates with dual variables updated from observed costs | Stochastic linear bandits with general anytime cumulative constraints · Theoretical analysis | regret | Helped |
| Use pessimistic dual updates with a time-varying tightness parameter | Stochastic linear bandits with general anytime cumulative constraints · Theoretical analysis | constraint violation | Helped |
| Use a primal-dual algorithm whose dual update scales with the number of constraints | Pessimistic-Optimistic Algorithm · Computational complexity | computational efficiency | Helped |
| Use the proposed algorithm instead of OPA | Constrained MAB example · Constrained MAB | regret | Helped |
| Use the proposed algorithm for inpatient flow routing | Inpatient flow routing · Capacity, fairness, and resource constraints | constraint violation | Helped |
| Use the proposed algorithm for inpatient flow routing | Inpatient flow routing · Capacity, fairness, and resource constraints | rounds until zero constraint violation: capacity, fairness and resource constraints are (32,50,54) | Helped |
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