Proposes an A/B testing estimator that introduces a hypothetical middle algorithm for stepwise estimation to induce positive correlation, reducing selection errors and halving required data volume.
InProceedings of The 25th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol
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A More Accurate Algorithm Comparison through A/B Testing using Offline Evaluation Methods
Proposes an A/B testing estimator that introduces a hypothetical middle algorithm for stepwise estimation to induce positive correlation, reducing selection errors and halving required data volume.