4 citations · 4 across the 12 of their papers we have counts for
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Powerful A/B-Testing Metrics and Where to Find Them
Olivier Jeunen, Shubham Baweja, Neeti Pokharna +1
Online controlled experiments, colloquially known as A/B-tests, are the bread and butter of real-world recommender system evaluation. Typically, end-users are randomly assigned som…
: 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…
Multi-Objective Recommendation via Multivariate Policy Learning
Olivier Jeunen, Jatin Mandav, Ivan Potapov +4
Real-world recommender systems often need to balance multiple objectives when deciding which recommendations to present to users. These include behavioural signals (e.g. clicks, sh…
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…
Learning-to-Rank with Nested Feedback
Hitesh Sagtani, Olivier Jeunen, Aleksei Ustimenko
Many platforms on the web present ranked lists of content to users, typically optimized for engagement-, satisfaction- or retention- driven metrics. Advances in the Learning-to-Ran…