  We present a general framework for classification of sparse and
irregularly-sampled time series. The properties of such time series can result
in substantial uncertainty about the values of the underlying temporal
processes, while making the data difficult to deal with using standard
classification methods that assume fixed-dimensional feature spaces. To address
these challenges, we propose an uncertainty-aware classification framework
based on a special computational layer we refer to as the Gaussian process
adapter that can connect irregularly sampled time series data to any black-box
classifier learnable using gradient descent. We show how to scale up the
required computations based on combining the structured kernel interpolation
framework and the Lanczos approximation method, and how to discriminatively
train the Gaussian process adapter in combination with a number of classifiers
end-to-end using backpropagation.
