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A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs
The algorithm combines ideas from the EXP3++ algorithm for stochastic and adversarial bandits and the EXP3. G algorithm for feedback graphs with a novel exploration scheme.
An Algorithm for Stochastic and Adversarial Bandits with Switching Costs
In the stochastically constrained adversarial regime, which includes the stochastic regime as a special case, it achieves a regret bound of $O\left(\big((\lambda K)^{2/3} T^{1/3} + \ln T\big)\sum_{i \neq i^*} \Delta_i^{-1}\right)$, where $\Delta_i$ are the suboptimality gaps and $i^*$ is a unique optimal arm.
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