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Found 3 papers, 2 papers with code
Faster Maximum Inner Product Search in High Dimensions
We provide theoretical guarantees that BanditMIPS returns the correct answer with high probability, while improving the complexity in $d$ from $O(\sqrt{d})$ to $O(1)$.
MABSplit: Faster Forest Training Using Multi-Armed Bandits
We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods.
BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits
Current state-of-the-art $k$-medoids clustering algorithms, such as Partitioning Around Medoids (PAM), are iterative and are quadratic in the dataset size $n$ for each iteration, being prohibitively expensive for large datasets.
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