1 research outputs found
Robust Prediction when Features are Missing
Predictors are learned using past training data which may contain features
that are unavailable at the time of prediction. We develop an approach that is
robust against outlying missing features, based on the optimality properties of
an oracle predictor which observes them. The robustness properties of the
approach are demonstrated on both real and synthetic data