Output Divergence Criterion for Active Learning in Collaborative Settings

In this paper, we address the task of active learning for linear regression models in collaborative settings. The goal of active learning is to select training points that would allow accurate prediction of test output values. We propose a new active learning criterion that is aimed at directly improving the accuracy of the output value estimation by analyzing the effect of the new training points on the estimates of the output values. The advantages of the proposed method are highlighted in collaborative settings – where most of the data points are missing, and the number of training data points is much smaller than the number of the parameters of the model.