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Found 7 papers, 0 papers with code
Robust Causal Bandits for Linear Models
Cumulative regret is adopted as the design criteria, based on which the objective is to design a sequence of interventions that incur the smallest cumulative regret with respect to an oracle aware of the entire causal model and its fluctuations.
Optimal Best Arm Identification with Fixed Confidence in Restless Bandits
It is shown that under every policy, the state-action visitation proportions satisfy a specific approximate flow conservation constraint and that these proportions match the optimal proportions dictated by the lower bound under any asymptotically optimal policy.
Best Arm Identification in Stochastic Bandits: Beyond $β-$optimality
Two key metrics for assessing bandit algorithms are computational efficiency and performance optimality (e. g., in sample complexity).
Active Sampling of Multiple Sources for Sequential Estimation
Additionally, each process $i\in\{1, \dots, K\}$ has a private parameter $\alpha_i$.
SPRT-based Efficient Best Arm Identification in Stochastic Bandits
Based on this test statistic, a BAI algorithm is designed that leverages the canonical sequential probability ratio tests for arm selection and is amenable to tractable analysis for the exponential family of bandits.
Best Arm Identification in Contaminated Stochastic Bandits
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
