Susan A. Murphy | DeepAI

DeepAI

Featured Co-authors

Joelle Pineau
116 publications
Finale Doshi-Velez
96 publications
Ambuj Tewari
72 publications
Lucas Janson
24 publications
Santosh Kumar
16 publications
Predrag Klasnja
14 publications
Inbal Nahum-Shani
12 publications
Walter Dempsey
11 publications
Peng Liao
8 publications
Kelly W. Zhang
7 publications
Tianchen Qian
7 publications

∙ 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 ...

Anna L. Trella, et al. ∙

∙ 06/08/2022

Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelines

Online reinforcement learning (RL) algorithms are increasingly used to p...

Anna L. Trella, et al. ∙

∙ 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...

Martin Cousineau, et al. ∙

∙ 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...

Kelly W. Zhang, et al. ∙

∙ 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...

Sarah Rathnam, et al. ∙

∙ 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...

Tianchen Qian, et al. ∙

∙ 04/29/2021

Statistical Inference with M-Estimators on Bandit Data

Bandit algorithms are increasingly used in real world sequential decisio...

Kelly W. Zhang, et al. ∙

∙ 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...

Ashley E. Walton, et al. ∙

∙ 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...

Tianchen Qian, et al. ∙

∙ 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 ...

Marianne Menictas, et al. ∙

∙ 02/08/2020

Inference for Batched Bandits

As bandit algorithms are increasingly utilized in scientific studies, th...

Kelly W. Zhang, et al. ∙

∙ 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...

Tianchen Qian, et al. ∙

∙ 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...

Tianchen Qian, et al. ∙

∙ 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...

Nicholas J. Seewald, et al. ∙

∙ 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...

Walter Dempsey, et al. ∙

∙ 06/28/2017

An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions

Increasing technological sophistication and widespread use of smartphone...

Huitian Lei, et al. ∙

∙ 02/14/2012

Active Learning for Developing Personalized Treatment

The personalization of treatment via bio-markers and other risk categori...

Kun Deng, et al. ∙