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Interpersonal conflict between couples is a significant source of stress with long-lasting effects on partners’ physical and psychological health. Motivated by findings in psychological science, we study how couples with distinct relationship functioning characteristics experience conflict in real life. We propose sub-population specific machine learning models using hierarchical and adaptive learning frameworks to automatically detect interpersonal conflict through the ambulatory monitoring of couples’ physiological signals, audio samples, and linguistic indices. Results indicate that the proposed models outperform a general model learned for the entire population and separate models independently trained on each sub-population, providing a foundation toward personalized health applications.