Near-optimal max-affine estimators for convex regression

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Convex-constrained Sparse Additive Modeling and Its Extensions (2017, background): “Note that we do not extrapolate the function values outside the range of the training data, because [BGS15] showed that a naive extrapolation can lead to large or even infinite generalization errors.”
Bayesian fractional posteriors (2016, methodology): “It is recent practice in the frequentist literature to avoid additional smoothness assumptions on convex functions while studying rates of convergence [3, 29].”