Machine Learning

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%.

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.

To combine supervised learning on data with reinforcement learning, we pre-train a supervised data prior, and penalize KL-divergence from this model using RL training. This enables effective learning of complex sequence-modeling problems for which we wish to match the data while optimizing external metrics like drug effectiveness. The approach produces compelling results in the disparate domains of music generation and drug discovery.

Machine learning applied to nightly data from sensors and smartphones, shows value for predicting college student’s mood the following evening. Using multi-task learning to simultaneously predicted related wellbeing factors like health, energy, stress, and alertness improves performance.

We collect observational data from real human conversations, and develop a measure of how much participants experienced bonding or chemistry. We analyze the effects of personality and attitudes on bonding, and find that attentiveness and excitement are more effective at promoting bonding than traits like attractiveness and humour.

Given only one-minute slices of facial expressions and body language, we use machine learning to accurately predict whether two humans having a conversation will bond with each other. We analyze factors which lead to bonding and discover that synchrony in body language and appropriate, empathetic facial expressions lead to higher bonding.

We use labels provided by domain experts to classify whether artifacts are present in an Electrodermal Activity signal. Through the use of active learning, we improve sample efficiency and reduce the burden on human experts by as much as 84%, while offering the same or improved performance.

Ambulatory measurement of Electrodermal Activity (EDA) from the wrist has important clinical benefits, such as predicting mood, stress, health, or even seizures. However, ambulatory measurement is noisy, and artifacts can easily be mistaken for true Skin Conductance Responses (SCRs). In addition to our paper which describes a machine learning method for detecting artifacts with 95% test accuracy, we built EDA Explorer, an open-source tool that allows users to automatically detect artifacts and SCRs within their data.

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.

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.