ProtoBandit: Efficient Prototype Selection via Multi-Armed Bandits. (arXiv:2210.01860v3 [cs.LG] UPDATED)

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 $\mathcal{S}$ that best represents a given target set
$\mathcal{T}$. Prototypical examples of a given dataset offer interpretable
insights into the underlying data distribution and assist in example-based
reasoning, thereby influencing every sphere of human decision-making. Current
state-of-the-art prototype selection approaches require
$O(|\mathcal{S}||\mathcal{T}|)$ similarity comparisons between source and
target data points, which becomes prohibitively expensive for …

arxiv multi-armed bandits