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Introduction to Part III: Markers to mHealth Predictors., , and . Mobile Health - Sensors, Analytic Methods, and Applications, Springer, (2017)Introduction to Part IV: Predictors to mHealth Interventions., , and . Mobile Health - Sensors, Analytic Methods, and Applications, Springer, (2017)Translating Behavioral Theory into Technological Interventions: Case Study of an mHealth App to Increase Self-reporting of Substance-Use Related Data., , , , , , , , , and 4 other author(s). CoRR, (2020)Just-in-Time but Not Too Much: Determining Treatment Timing in Mobile Health., , , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2 (4): 179:1-179:21 (2018)IntelligentPooling: practical Thompson sampling for mHealth., , , and . Mach. Learn., 110 (9): 2685-2727 (2021)reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use., , , , , , , and . CoRR, (2024)Control Engineering Methods for the Design of Robust Behavioral Treatments., , , and . IEEE Trans. Control. Syst. Technol., 25 (3): 979-990 (2017)Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning., , , , and . CoRR, (2024)Effect-Invariant Mechanisms for Policy Generalization., , , , and . CoRR, (2023)iSurvive: An Interpretable, Event-time Prediction Model for mHealth., , , , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 970-979. PMLR, (2017)