ArticleProceedings of machine learning research2021
Power Constrained Bandits.
Article in Proceedings of machine learning research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- How Notifications Affect Engagement With a Behavior Change App: Results From a Micro-Randomized Trial.JMIR mHealth and uHealth · 2023Trial
- Adaptive Randomization in Conjoint Survey Experiments.Political analysis : an annual publication of the Methodology Section of the American Political Science Association · 2026Article
- Automated Personalized Goal Setting for Individual Exercise Behavior: Protocol for a Web-Based Adaptive Intervention Trial.JMIR research protocols · 2025Article
- Replicable Bandits for Digital Health Interventions.Statistical science : a review journal of the Institute of Mathematical Statistics · 2025Article
- Online learning in bandits with predicted context.Proceedings of machine learning research · 2024Article
- Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health.Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence · 2024Article
- Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care.Proceedings of the ... Innovative Applications of Artificial Intelligence Conference. Innovative Applications of Artificial Intelligence Conference · 2023Article
- Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines.Algorithms · 2022Article
- Statistical Inference with M-Estimators on Adaptively Collected Data.Advances in neural information processing systems · 2021Article
- Inference for Batched Bandits.Advances in neural information processing systems · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Contextual bandits often provide simple and effective personalization in decision making problems, making them popular tools to deliver personalized interventions in mobile health as well as other health applications. However, when bandits are deployed in the context of a scientific study-e.g. a clinical trial to test if a mobile health intervention is effective-the aim is not only to personalize for an individual, but also to determine, with sufficient statistical power, whether or not the system's intervention is effective. It is essential to assess the effectiveness of the intervention before broader deployment for better resource allocation. The two objectives are often deployed under different model assumptions, making it hard to determine how achieving the personalization and statistical power affect each other. In this work, we develop general meta-algorithms to modify existing algorithms such that sufficient power is guaranteed while still improving each user's well-being. We also demonstrate that our meta-algorithms are robust to various model mis-specifications possibly appearing in statistical studies, thus providing a valuable tool to study designers.
Identifiers
34927078PMC8675738What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.