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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.18744 |
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| _version_ | 1866911029339881472 |
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| author | Feng, Qing Daulton, Samuel Letham, Benjamin Balandat, Maximilian Bakshy, Eytan |
| author_facet | Feng, Qing Daulton, Samuel Letham, Benjamin Balandat, Maximilian Bakshy, Eytan |
| contents | Online experiments in internet systems, also known as A/B tests, are used for a wide range of system tuning problems, such as optimizing recommender system ranking policies and learning adaptive streaming controllers. Decision-makers generally wish to optimize for long-term treatment effects of the system changes, which often requires running experiments for a long time as short-term measurements can be misleading due to non-stationarity in treatment effects over time. The sequential experimentation strategies--which typically involve several iterations--can be prohibitively long in such cases. We describe a novel approach that combines fast experiments (e.g., biased experiments run only for a few hours or days) and/or offline proxies (e.g., off-policy evaluation) with long-running, slow experiments to perform sequential, Bayesian optimization over large action spaces in a short amount of time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18744 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments Feng, Qing Daulton, Samuel Letham, Benjamin Balandat, Maximilian Bakshy, Eytan Machine Learning Online experiments in internet systems, also known as A/B tests, are used for a wide range of system tuning problems, such as optimizing recommender system ranking policies and learning adaptive streaming controllers. Decision-makers generally wish to optimize for long-term treatment effects of the system changes, which often requires running experiments for a long time as short-term measurements can be misleading due to non-stationarity in treatment effects over time. The sequential experimentation strategies--which typically involve several iterations--can be prohibitively long in such cases. We describe a novel approach that combines fast experiments (e.g., biased experiments run only for a few hours or days) and/or offline proxies (e.g., off-policy evaluation) with long-running, slow experiments to perform sequential, Bayesian optimization over large action spaces in a short amount of time. |
| title | Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.18744 |