research
∙
06/20/2023
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
Discount regularization, using a shorter planning horizon when calculati...
share
research
∙
05/17/2023
Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions
Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized...
research
∙
08/15/2022
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-Care
Dental disease is one of the most common chronic diseases despite being ...
research
∙
06/08/2022
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines
Online reinforcement learning (RL) algorithms are increasingly used to p...
research
∙
02/28/2022
Estimating causal effects with optimization-based methods: A review and empirical comparison
In the absence of randomized controlled and natural experiments, it is n...
research
∙
02/14/2022
Statistical Inference After Adaptive Sampling in Non-Markovian Environments
There is a great desire to use adaptive sampling methods, such as reinfo...
research
∙
09/16/2021
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning
In batch reinforcement learning, there can be poorly explored state-acti...
research
∙
07/08/2021
The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations
Just-in-time adaptive interventions (JITAIs) are time-varying adaptive i...
research
∙
04/29/2021
Statistical Inference with M-Estimators on Bandit Data
Bandit algorithms are increasingly used in real world sequential decisio...
research
∙
04/23/2020
The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design Considerations
Just-in-time adaptive interventions (JITAIs) are time-varying adaptive i...
research
∙
04/21/2020
The Micro-Randomized Trial for Developing Digital Interventions: Data Analysis Methods
Although there is much excitement surrounding the use of mobile and wear...
research
∙
03/28/2020
Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health
To effect behavior change a successful algorithm must make high-quality ...
research
∙
02/08/2020
Inference for Batched Bandits
As bandit algorithms are increasingly utilized in scientific studies, th...
research
∙
06/03/2019
Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary Outcomes
Advances in wearables and digital technology now make it possible to del...
research
∙
02/28/2019
Linear mixed models under endogeneity: modeling sequential treatment effects with application to a mobile health study
Mobile health is a rapidly developing field in which behavioral treatmen...
research
∙
12/27/2018
Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials
There is a growing interest in leveraging the prevalence of mobile techn...
research
∙
11/09/2017
The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments
Technological advancements in the field of mobile devices and wearable s...
research
∙
06/28/2017
An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions
Increasing technological sophistication and widespread use of smartphone...
research
∙
02/14/2012