| 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 | 97 | 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 | 59 | 2020 |
| Randomised trials for the Fitbit generation W Dempsey, P Liao, P Klasnja, I Nahum‐Shani, SA Murphy Significance 12 (6), 20-23, 2015 | 44 | 2015 |
| 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 | 29 | 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 | 25 | 2020 |
| 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 | 23 | 2021 |
| Group-driven reinforcement learning for personalized mhealth intervention F Zhu, J Guo, Z Xu, P Liao, L Yang, J Huang International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 20 | 2018 |
| Batch policy learning in average reward markov decision processes P Liao, Z Qi, P Klasnja, S Murphy arXiv preprint arXiv:2007.11771, 2020 | 19 | 2020 |
| Intelligentpooling: Practical thompson sampling for mhealth S Tomkins, P Liao, P Klasnja, S Murphy Machine learning 110 (9), 2685-2727, 2021 | 11 | 2021 |
| Effective warm start for the online actor-critic reinforcement learning based mhealth intervention F Zhu, P Liao arXiv preprint arXiv:1704.04866, 2017 | 10 | 2017 |
| Constructing just-in-time adaptive interventions P Liao, A Tewari, S Murphy Phd Section Proposal, 1-49, 2015 | 8 | 2015 |
| 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 | 7 | 2021 |
| 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 | 7 | 2017 |
| 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 | 4 | 2020 |
| Intelligent pooling in Thompson sampling for rapid personalization in mobile health S Tomkins, P Liao, S Yeung, P Klasnja, S Murphy | 2 | 2019 |
| Robust Batch Policy Learning in Markov Decision Processes Z Qi, P Liao arXiv preprint arXiv:2011.04185, 2020 | 1 | 2020 |
| Just-In-Time Adaptive Interventions: Experiment, Inference and Online Learning P Liao | | 2019 |
| 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 | | 2018 |
