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Found 3 papers, 1 papers with code
Nested conformal prediction and quantile out-of-bag ensemble methods
Conformal prediction is a popular tool for providing valid prediction sets for classification and regression problems, without relying on any distributional assumptions on the data.
First order expansion of convex regularized estimators
Such first order expansion implies that the risk of $\hat{\beta}$ is asymptotically the same as the risk of $\eta$ which leads to a precise characterization of the MSE of $\hat\beta$; this characterization takes a particularly simple form for isotropic design.
On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors
The rate of convergence of the least squares estimator (LSE) for the unknown regression function is well studied when the errors are sub-Gaussian.
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