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ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits
In this work, we propose a multi-armed bandit-based framework for identifying a compact set of informative data instances (i. e., the prototypes) from a source dataset $S$ that best represents a given target set $T$.
PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
The problem of identifying $k > 1$ distinct arms from the best $\rho$ fraction is not always well-defined; for a special class of this problem, we present lower and upper bounds.
Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory
We present a conceptually simple, and efficient algorithm that needs to remember statistics of at most $M$ arms, and for any $K$-armed finite bandit instance it enjoys a $O(KM +K^{1. 5}\sqrt{T\log (T/MK)}/M)$ upper-bound on regret.
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