Learning Bounds for Risk-sensitive Learning

CV aR minimization algorithm to account for the covariate shift in the data-generating distribution. The advantage of risk-sensitive (either risk-seeking or risk-averse) objectives in machine learning, however, is not limited to tasks involving social considerations. Indeed, there exists a rich body of works which implicitly propose to minimize risk-sensitive measures of loss, as a technique to better optimize the standard expected loss.