A general framework for label-efficient online evaluation with asymptotic guarantees

12 Jun 2020Neil G. MarchantBenjamin I. P. Rubinstein

Achieving statistically significant evaluation with passive sampling of test data is challenging in settings such as extreme classification and record linkage, where significant class imbalance is prevalent. Adaptive importance sampling focuses labeling on informative regions of the instance space, however it breaks data independence assumptions - commonly required for asymptotic guarantees that assure estimates approximate population performance and provide practical confidence intervals... (read more)

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