  This research arises from the need to predict the amount of air pollutants in
meteorological stations. Air pollution depends on the location of the stations
(weather conditions and activities in the surroundings). Frequently, the
surrounding information is not considered in the learning process. This
information is known beforehand in the absence of unobserved weather conditions
and remains constant for the same station. Considering the surrounding
information as side information facilitates the generalization for predicting
pollutants in new stations, leading to a zero-shot regression scenario.
Available methods in zero-shot typically lean towards classification, and are
not easily extensible to regression. This paper proposes two zero-shot methods
for regression. The first method is a similarity based approach that learns
models from features and aggregates them using side information. However,
potential knowledge of the feature models may be lost in the aggregation. The
second method overcomes this drawback by replacing the aggregation procedure
and learning the correspondence between side information and feature-induced
models, instead. Both proposals are compared with a baseline procedure using
artificial datasets, UCI repository communities and crime datasets, and the
pollutants. Both approaches outperform the baseline method, but the parameter
learning approach manifests its superiority over the similarity based method.
