Tensorized Multi-Task Learning for Personalized Modeling of Heterogeneous Individuals with High-Dimensional Data

Model personalization, with broad applications in several fields, including precision medicine and healthcare (Johnson et al., 2021; Hilton et al., 2020; Abul-Husn and Kenny, 2019), advertising (Bilenko and Richardson, 2011), and interactive user inter faces (Ma et al., 2021), involves tailoring models to account for the unique characteristics and featur es of individuals (or subgroups) within a population. A key challenge in achieving model personaliza tion is addressing heterogeneity among individuals while leveraging their similarities. When eac h individual has access to a large amount of data, leveraging similarity is not essential, and one strai ghtforward method is to fit separate models to each individual, allowing for fully individualized m odeling. However, in most applications, including healthcare, access to a large sample size for each individual is difficult and expensive. An alternative approach to fully individualized modeling tra ins one model to fit all by combining data from all individuals. While this approach increases the sam ple size, it overlooks the unique traits of individuals and the variations between them. Therefore, middle-ground methods that can use shared information and capture data heterogeneity are nece ssary. An example of such a trade-off appears in telemonitoring appl ications for chronic disease management, such as remote assessment of Parkinson's disease s everity using smartphone-based sensor 1