Learning high-dimensional mixed models via amortized variational inference

Modelling longitudinal data is an important yet challenging task. These datasets can be high-dimensional, consist of non-linear effects, and contain time-varying covariates. In this work, we leverage linear mixed models (LMMs) and amortized variational inference to provide conditional priors for VAEs, and propose LMM-VAE, a model that is scalable, interpretable, and shares theoretical connections to the GP-based VAEs. We empirically demonstrate that LMM-VAE performs competitively compared to existing approaches.