Detection of Abnormal Input-Output Associations
  We study a novel outlier detection problem that aims to identify abnormal
input-output associations in data, whose instances consist of multi-dimensional
input (context) and output (responses) pairs. We present our approach that
works by analyzing data in the conditional (input--output) relation space,
captured by a decomposable probabilistic model. Experimental results
demonstrate the ability of our approach in identifying multivariate conditional
outliers.
