Multi-task Learning

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

Predicting Tomorrow’s Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation

Modeling measures like mood, stress, and health using a monolithic machine learning model leads to low prediction accuracy. Instead, we develop personalized regression models using multi-task learning and Gaussian Processes, leading to dramatic improvements in next-day predictions.

Natasha Jaques, O. Rudovic, S. Taylor, A. Sano, R. Picard

2017 In Proceedings of Machine Learning Research

Predicting Tomorrow’s Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation

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