Huber loss

Summary

The Huber loss is a loss function that is quadratic for small residuals and linear for large ones. It combines the efficiency of least squares for well-behaved data with the resistance of absolute loss to outliers. Peter Huber introduced it in 1964 to define a robust M-estimator of location, and it is now also common in regression and machine learning.