Power Constrained Bandits
- URL: http://arxiv.org/abs/2004.06230v4
- Date: Tue, 27 Jul 2021 07:55:49 GMT
- Title: Power Constrained Bandits
- Authors: Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan Murphy, Finale
Doshi-Velez
- Abstract summary: We develop general meta-algorithms to modify existing algorithms.
Our meta-algorithms are robust to various model mis-specifications possibly appearing in statistical studies.
- Score: 46.44025793243983
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- Allocation Requires Prediction Only if Inequality Is Low [24.57131078538418]
We evaluate the efficacy of prediction-based allocations in settings where individuals belong to larger units.
We find that prediction-based allocations outperform baseline methods only when between-unit inequality is low and the intervention budget is high.
arXiv Detail & Related papers (2024-06-19T23:23:32Z) - Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
In practice, causal researchers do not have a single outcome in mind a priori.
In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty.
We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
arXiv Detail & Related papers (2024-04-29T08:16:30Z) - Adaptive Interventions with User-Defined Goals for Health Behavior Change [17.688448640253494]
Mobile health applications present a promising avenue for low-cost, scalable health behavior change promotion.
tailoring advice to a person's unique goals, preferences, and life circumstances is a critical component of health coaching.
We introduce a new Thompson sampling algorithm that can accommodate personalized reward functions.
arXiv Detail & Related papers (2023-11-16T01:00:04Z) - Policy Optimization for Personalized Interventions in Behavioral Health [8.10897203067601]
Behavioral health interventions, delivered through digital platforms, have the potential to significantly improve health outcomes.
We study the problem of optimizing personalized interventions for patients to maximize a long-term outcome.
We present a new approach for this problem that we dub DecompPI, which decomposes the state space for a system of patients to the individual level.
arXiv Detail & Related papers (2023-03-21T21:42:03Z) - Safe AI for health and beyond -- Monitoring to transform a health
service [51.8524501805308]
We will assess the infrastructure required to monitor the outputs of a machine learning algorithm.
We will present two scenarios with examples of monitoring and updates of models.
arXiv Detail & Related papers (2023-03-02T17:27:45Z) - Zero-shot causal learning [64.9368337542558]
CaML is a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task.
We show that CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training.
arXiv Detail & Related papers (2023-01-28T20:14:11Z) - Adaptive Identification of Populations with Treatment Benefit in
Clinical Trials: Machine Learning Challenges and Solutions [78.31410227443102]
We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial.
We propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction.
arXiv Detail & Related papers (2022-08-11T14:27:49Z) - In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image
Domains [22.92165116962952]
In-bed human posture estimation provides important health-related metrics with potential value in medical condition assessments.
We propose a multi-modal conditional variational autoencoder (MC-VAE) capable of reconstructing features from missing modalities seen during training.
We demonstrate that body positions can be effectively recognized from the available modality, achieving on par results with baseline models.
arXiv Detail & Related papers (2021-11-30T04:56:16Z) - Adversarial Sample Enhanced Domain Adaptation: A Case Study on
Predictive Modeling with Electronic Health Records [57.75125067744978]
We propose a data augmentation method to facilitate domain adaptation.
adversarially generated samples are used during domain adaptation.
Results confirm the effectiveness of our method and the generality on different tasks.
arXiv Detail & Related papers (2021-01-13T03:20:20Z) - Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile
Health [3.8974425658660596]
A mobile health (mHealth) application designed to increase physical activity must make contextually relevant suggestions to motivate users.
We propose an algorithm which provides users with contextualized and personalized physical activity suggestions.
We show improvements over state of the art approaches both in speed and accuracy of up to 99% and 56% respectively.
arXiv Detail & Related papers (2020-03-28T19:57:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.