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Budgeted Learning for Developing Personalized Treatment., , and . ICMLA, page 7-14. IEEE, (2014)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)Personalizing Intervention Probabilities By Pooling., , and . CoRR, (2018)A Generalization Error for Q-Learning.. J. Mach. Learn. Res., (2005)iSurvive: An Interpretable, Event-time Prediction Model for mHealth., , , , , , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 970-979. PMLR, (2017)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)Control Engineering Methods for the Design of Robust Behavioral Treatments., , , and . IEEE Trans. Control. Syst. Technol., 25 (3): 979-990 (2017)