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Finding the Sweet Spot(s): Understanding Context to Support Physical Activity Plans., , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2 (1): 29:1-29:17 (2018)The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design Considerations., , , , , , and . CoRR, (2020)IntelligentPooling: Practical Thompson Sampling for mHealth., , , and . CoRR, (2020)Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity., , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 4 (1): 18:1-18:22 (2020)Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling., , , , , , , and . CoRR, (2023)Doubly robust nearest neighbors in factor models., , , , , and . CoRR, (2022)Action Centered Contextual Bandits., , , and . NIPS, page 5977-5985. (2017)Model Personalization in Behavioral Interventions using Model-on-Demand Estimation and Discrete Simultaneous Perturbation Stochastic Approximation., , , and . ACC, page 671-676. IEEE, (2022)I give up! Five factors that contribute to the abandonment of information management strategies., , , and . ASIST, volume 45 of Proc. Assoc. Inf. Sci. Technol., page 1-6. Wiley, (2008)Design Lessons from a Micro-Randomized Pilot Study in Mobile Health., , , , , , and . Mobile Health - Sensors, Analytic Methods, and Applications, Springer, (2017)