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In this paper, we propose a novel {\em $e$-exponentiated} transformation, $0\le e<1$, for loss functions. When the transformation is applied to a convexloss function, the transformed loss function become more robust to outliers.Using a novel generalization error bound, we have theoretically shown that thetransformed loss function has a tighter bound for datasets corrupted byoutliers. Our empirical observation shows that the accuracy obtained using thetransformed loss function can be significantly better than the same obtainedusing the original loss function and comparable to that obtained by some otherstate of the art methods in the presence of label noise.

Making Convex Loss Functions Robust to Outliers using $e$-Exponentiated Transformation