We propose a novel second order cone programming formulation for designing robust classifiers
which can handle uncertainty in observations. Similar formulations are also derived for designing
regression functions which are robust to uncertainties in the regression setting. The proposed formulations
are independent of the underlying distribution, requiring only the existence of second order
moments. These formulations are then specialized to the case of missing values in observations
for both classification and regression problems. Experiments show that the proposed formulations
outperform imputation