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Always-On Experimentation

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arXiv Machine Learning
5d ago

Offline Policy Evaluation as a decision support tool for designing Adaptive Experiments

The paper explores how data from fixed A/B tests can guide the deployment of adaptive experiments using contextual bandits. By combining off‑policy evaluation with a controlled warm‑start simulation, the authors rank pre‑specified adaptive and non‑adaptive policies using doubly robust estimators. Experiments on synthetic trials and real benchmarks show that adaptive, context‑aware policies outperform fixed allocations when heterogeneity exists, but offer little advantage otherwise.

By Jo\~ao Victor Ferreira Alves, Eduardo Rocha Laurentino, Gustavo de Oliveira Kanno, Thiago Costa Rizuti da Rocha
arXiv AI
Jun 15

CARE: Controlling LLM-Generated Policies through Auditable Review of Evidence in Scientific Experimentation

arXiv:2606. 14581v1 Announce Type: cross Abstract: Granting LLMs direct control over costly, irreversible scientific experiments leads to unsafe exploration and unstable performance, but discarding LLM creativity entirely sacrifices significant optimization potential.

By Guanyu Liu, Weiyi Kong, Zeyu Wang, Boer Zhang, Baiqing Li, Peiyu Zhang, Tianyu Shi
arXiv Machine Learning
Jul 13

Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation

arXiv:2506. 03062v2 Announce Type: replace Abstract: A/B tests in online experiments face statistical power challenges when testing multiple candidates simultaneously, while adaptive experimental designs (AED) alone fall short in inferring experiment statistics such as the average treatment effect, especially with many metrics (e.

By Qining Zhang, Tanner Fiez, Yi Liu, Wenyang Liu
arXiv Machine Learning
Jul 13

Global Sequential Testing for Multi-Stream Auditing

arXiv:2602. 21479v3 Announce Type: replace-cross Abstract: Across many risk-sensitive areas, it is critical to continuously audit machine learning systems as we receive more data to quickly determine if they are performing as designed.

By Beepul Bharti, Ambar Pal, Jeremias Sulam