Title: Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction,
Abstract: Missing data and noisy observations pose significant challenges for reliably
predicting events from irregularly sampled multivariate time series
(longitudinal) data. Imputation methods, which are typically used for
completing the data prior to event prediction, lack a principled mechanism to
account for the uncertainty due to missingness. Alternatively, state-of-the-art
joint modeling techniques can be used for jointly modeling the longitudinal and
event data and compute event probabilities conditioned on the longitudinal
observations. These approaches, however, make strong parametric assumptions and
do not easily scale to multivariate signals with many observations. Our
proposed approach consists of several key innovations. First, we develop a
flexible and scalable joint model based upon sparse multiple-output Gaussian
processes. Unlike state-of-the-art joint models, the proposed model can explain
highly challenging structure including non-Gaussian noise while scaling to
large data. Second, we derive an optimal policy for predicting events using the
distribution of the event occurrence estimated by the joint model. The derived
policy trades-off the cost of a delayed detection versus incorrect assessments
and abstains from making decisions when the estimated event probability does
not satisfy the derived confidence criteria. Experiments on a large dataset
show that the proposed framework significantly outperforms state-of-the-art
techniques in event prediction.