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We present a general framework for classification of sparse andirregularly-sampled time series. The properties of such time series can resultin substantial uncertainty about the values of the underlying temporalprocesses, while making the data difficult to deal with using standardclassification methods that assume fixed-dimensional feature spaces. To addressthese challenges, we propose an uncertainty-aware classification frameworkbased on a special computational layer we refer to as the Gaussian processadapter that can connect irregularly sampled time series data to any black-boxclassifier learnable using gradient descent. We show how to scale up therequired computations based on combining the structured kernel interpolationframework and the Lanczos approximation method, and how to discriminativelytrain the Gaussian process adapter in combination with a number of classifiersend-to-end using backpropagation.

A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification