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.
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , pages =
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
INDEQS is a graph-informed NCDE variant that separates inner hidden-state mixing from outer vector-field mixing and reports lower MAE than uninformed NCDEs on synthetic advection data and real river/traffic tasks when the graph is known.
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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.
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INDEQS: Informed Neural controlled Differential EQuationS
INDEQS is a graph-informed NCDE variant that separates inner hidden-state mixing from outer vector-field mixing and reports lower MAE than uninformed NCDEs on synthetic advection data and real river/traffic tasks when the graph is known.