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arXiv Machine Learning By Malte Londschien

CVaR anchor regression protects against rare shifts

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The paper introduces CVaR anchor regression, a method that replaces the average squared mean residual used in anchor regression with a tail average to better protect against rare, large shifts in training data. This approach avoids over‑weighting environments with high noise levels and, under a linear structural model with heteroscedastic noise, provides an exact worst‑case risk guarantee. For discrete environments, decreasing the CVaR tail fraction expands the robustness set from an ellipsoid to a scaled convex hull of training shifts and their negatives, allowing improved protection against rare shifts while maintaining accuracy on common environments, as demonstrated on New York City taxi data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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