The PDF file includes:

• Appendix A. Supplementary tables and figures for numerical experiments.
• Appendix B. Supplementary tables for data applications.
• Appendix C. Methods for confidence region construction experiments.
• Appendix D. Neural network architectures in numerical experiments.
• Appendix E. Further discussion of guarantees for nested minimax algorithms.
• Appendix F. An example showing challenges faced by existing nested maximin algorithms.
• Appendix G. Captions for additional file types.
• Fig. S1. Convergence of the risk of the learned estimators in the Gaussian estimation example.
• Fig. S2. Pointwise quantiles of the fit of our learned two-layer multilayer perceptron prediction function at W₂ = 0 and different values of W₁ based on n = 50 observations from four data-generating distributions.
• Fig. S3. Learning curves for worst-case (red) and uniform-prior Bayes (blue) prediction performance in the logistic regression settings i to xii shown in Table 1.
• Fig. S4. Performance of the learned 95% level confidence region procedure across different values of η.
• Fig. S5. Prior generator multilayer perceptrons and procedure multilayer perceptrons used for the point estimation examples.
• Fig. S6. Estimator LSTM used when estimating binary regressions.
• Fig. S7. Estimator LSTM used when defining the interior point of our confidence regions.
• Table S1. Gaussian model with σ² = 1, |μ| ≤ m, and n = 1.
• Table S2. Final estimated performance of the learned prediction algorithms, the MLE, and a linear-logistic regression.
• Table S3. Performance of our learned procedures and of existing procedures in data illustrations.
• References (48–50)

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Other Supplementary Material for this manuscript includes the following:

• Movie S1 (.mp4 format). Evolution of the risk of the learned estimator of μ as the weights of the neural network are updated in the Gaussian model with n = 50 observations and unknown (μ, σ).