Online Learning for Non-Stationary A/B Tests

Andrés Muñoz Medina; Sergei Vassilvitskii; Dong Yin

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arXiv · 1802.05315

Online Learning for Non-Stationary A/B Tests

Andrés Muñoz Medina·Sergei Vassilvitskii·Dong Yin

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Abstract

The rollout of new versions of a feature in modern applications is a manual multi-stage process, as the feature is released to ever larger groups of users, while its performance is carefully monitored. This kind of A/B testing is ubiquitous, but suboptimal, as the monitoring requires heavy human intervention, is not guaranteed to capture consistent, but short-term fluctuations in performance, and is inefficient, as better versions take a long time to reach the full population. In this work we formulate this question as that of expert learning, and give a new algorithm Follow-The-Best-Interval, FTBI, that works in dynamic, non-stationary environments. Our approach is practical, simple, and efficient, and has rigorous guarantees on its performance. Finally, we perform a thorough evaluation on synthetic and real world datasets and show that our approach outperforms current state-of-the-art methods.

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Andrés Muñoz Medina, Sergei Vassilvitskii, Dong Yin. 2018-02-14. Online Learning for Non-Stationary A/B Tests. https://arxiv.org/abs/1802.05315

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