Peng Liao
Peng Liao
Harvard University
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Cited by
Cited by
Sample size calculations for micro‐randomized trials in mHealth
P Liao, P Klasnja, A Tewari, SA Murphy
Statistics in medicine 35 (12), 1944-1971, 2016
Personalized heartsteps: A reinforcement learning algorithm for optimizing physical activity
P Liao, K Greenewald, P Klasnja, S Murphy
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2020
Randomised trials for the Fitbit generation
W Dempsey, P Liao, P Klasnja, I Nahum‐Shani, SA Murphy
Significance 12 (6), 20-23, 2015
Off-policy estimation of long-term average outcomes with applications to mobile health
P Liao, P Klasnja, S Murphy
Journal of the American Statistical Association 116 (533), 382-391, 2021
Batch policy learning in average reward markov decision processes
P Liao, Z Qi, R Wan, P Klasnja, SA Murphy
The Annals of Statistics 50 (6), 3364-3387, 2022
Just-in-time but not too much: Determining treatment timing in mobile health
P Liao, W Dempsey, H Sarker, SM Hossain, M Al'Absi, P Klasnja, ...
Proceedings of the ACM on interactive, mobile, wearable and ubiquitous …, 2018
The stratified micro-randomized trial design: sample size considerations for testing nested causal effects of time-varying treatments
W Dempsey, P Liao, S Kumar, SA Murphy
The annals of applied statistics 14 (2), 661, 2020
Group-driven reinforcement learning for personalized mhealth intervention
F Zhu, J Guo, Z Xu, P Liao, L Yang, J Huang
Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018
Sense2Stop: a micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention
SL Battalio, DE Conroy, W Dempsey, P Liao, M Menictas, S Murphy, ...
Contemporary Clinical Trials 109, 106534, 2021
Intelligentpooling: Practical thompson sampling for mhealth
S Tomkins, P Liao, P Klasnja, S Murphy
Machine learning 110 (9), 2685-2727, 2021
Effective warm start for the online actor-critic reinforcement learning based mhealth intervention
F Zhu, P Liao
arXiv preprint arXiv:1704.04866, 2017
Constructing just-in-time adaptive interventions
P Liao, A Tewari, S Murphy
Phd Section Proposal, 1-49, 2015
Cohesion-based online actor-critic reinforcement learning for mhealth intervention
F Zhu, P Liao, X Zhu, Y Yao, J Huang
arXiv preprint arXiv:1703.10039, 2017
Robust batch policy learning in markov decision processes
Z Qi, P Liao
arXiv preprint arXiv:2011.04185, 2020
Rapidly personalizing mobile health treatment policies with limited data
S Tomkins, P Liao, P Klasnja, S Yeung, S Murphy
arXiv preprint arXiv:2002.09971, 2020
Intelligent pooling in Thompson sampling for rapid personalization in mobile health
S Tomkins, P Liao, S Yeung, P Klasnja, S Murphy
Cohesion-driven Online Actor-Critic Reinforcement Learning for mHealth Intervention
F Zhu, P Liao, X Zhu, J Yao, J Huang
Proceedings of the 2018 ACM International Conference on Bioinformatics …, 2018
Just-In-Time Adaptive Interventions: Experiment, Inference and Online Learning
P Liao
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