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This paper initiates the study of active learning for exact recovery ofpartitions exclusively through access to a same-cluster oracle in the presenceof bounded adversarial error. We first highlight a novel connection betweenlearning partitions and correlation clustering. Then we use this connection tobuild a R\'enyi-Ulam style analytical framework for this problem, and proveupper and lower bounds on its worst-case query complexity. Further, we boundthe expected performance of a relevant randomized algorithm. Finally, we studythe relationship between adaptivity and query complexity for this problem andrelated variants.

Error-Tolerant Exact Query Learning of Finite Set Partitions with Same-Cluster Oracle