Corpus ID: 34179195

Adaptive Sampling for Clustered Ranking

@inproceedings{Katariya2017AdaptiveSF,
  title={Adaptive Sampling for Clustered Ranking},
  author={S. Katariya and Lalit Jain and Nandana Sengupta and James Evans and R. Nowak},
  year={2017}
}
We consider the problem of sequential or active clustered ranking, where the goal is to sort items according to their means into clusters of pre-specified sizes, by adaptively sampling from their reward distributions. This setting is useful in many social science applications, where an approximate rank of every item is desired. In contrast, a complete ranking of the items requires a large number of samples if the means are close, while only finding the top few items as is done in recommender… Expand

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