Kernel Methods

Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health

Traditional, one-size-fits-all machine learning models fail to account for individual differences in predicting wellbeing outcomes like stress, mood, and health. Instead, we personalize models to the individual using multi-task learning (MTL), employing hierarchical Bayes, kernel-based and deep neural network MTL models to improve prediction accuracy by 13-23%.

Natasha Jaques*, S. Taylor*, E. Nosakhare, A. Sano, R. Picard

2017 In IEEE Transactions on Affective Computing (TAFFC) Best Paper; NeurIPS Machine Learning for Healthcare (ML4HC) Workshop Best Paper

Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health

Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing

Wellbeing is a complex internal state consisting of several related dimensions, such as happiness, stress, energy, and health. We use Multi-task Multi-kernel learning to classify them simultaneously, leading to significant performance approvements.

Natasha Jaques*, S. Taylor*, A. Sano, R. Picard

2015 In Neural Information Processing Systems (NeurIPS) Workshop on Multimodal Machine Learning

Multi-task Multi-Kernel Learning for Estimating Individual Wellbeing

Predicting students' happiness from physiology, phone, mobility, and behavioral data

We train machine learning models to predict students’ happiness from extensive data comprising physiological signals, location, smartphone logs, and behavioral questions. Analyzing which features provide the highest information gain reveals that skin conductance during sleep, social interaction, exercise, and fewer phone screen hours are all positively associated with happiness.

Natasha Jaques*, S. Taylor*, A. Azaria, A. Ghandeharioun, A. Sano, R. Picard

2015 In International Conference on Affective Computing and Intelligent Interaction (ACII)

Predicting students' happiness from physiology, phone, mobility, and behavioral data