We study prediction in new environments when training data contain rare, large shifts.
Anchor regression penalizes the average of the squared mean residual across environments. It protects against shifts in an ellipsoid determined by the second moment of the training shifts.
Covering rare shifts may therefore require a large penalty, expanding the ellipsoid in every direction and reducing accuracy on common environments.
We propose CVaR anchor regression
CVaR anchor regression, which replaces the average of the squared mean residuals with a tail average.
Unlike CVaR or GroupDRO applied directly to prediction risks, it does not give environments more weight solely because their noise levels are high.
We prove an exact worst-case risk guarantee under a linear structural model that allows for heteroscedastic noise.
For discrete environments
Decreasing the CVaR tail fraction expands the robustness set from an ellipsoid to a scaled convex hull of the training shifts and their negatives.
A separate parameter controls its scale.
Examples
Examples show how the method can improve protection against rare shifts while retaining accuracy on common environments.
We illustrate the method on New York City taxi data.