4 citations · 4 across the 12 of their papers we have counts for
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Variance Reduction for Heavy-Tailed Monetization Metrics in Ranking Experiments via Post-Stratification
Neeti Pokharna, Olivier Jeunen, Yatharth Saraf +1
Online evaluation of ranking and retrieval systems often relies on downstream monetization metrics such as app revenue or creator earnings. These metrics are typically heavy-tailed…
: Off-Policy Estimation with Pairs of Policies
Olivier Jeunen, Aleksei Ustimenko
The off-policy paradigm casts recommendation as a counterfactual decision-making task, allowing practitioners to unbiasedly estimate online metrics using offline data. This leads t…
Learning Metrics that Maximise Power for Accelerated A/B-Tests
Olivier Jeunen, Aleksei Ustimenko
Online controlled experiments are a crucial tool to allow for confident decision-making in technology companies. A North Star metric is defined (such as long-term revenue or user r…
Variance Reduction in Ratio Metrics for Efficient Online Experiments
Shubham Baweja, Neeti Pokharna, Aleksei Ustimenko +1
Online controlled experiments, such as A/B-tests, are commonly used by modern tech companies to enable continuous system improvements. Despite their paramount importance, A/B-tests…