Huber Loss is a robust loss function used in regression problems that is less sensitive to outliers than the squared error loss. It combines the ideas of mean squared error and mean absolute error, providing a smooth transition between the two by introducing a parameter that determines the point where the loss changes from quadratic to linear.
Outlier robustness refers to the ability of statistical methods and models to maintain their performance in the presence of outliers, which are data points that deviate significantly from the rest of the dataset. Techniques that enhance outlier robustness are crucial for ensuring that the insights and predictions derived from data remain reliable and accurate, even when the data contains anomalies or errors.