AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Missing covariates are frequently encountered in supervised learning problems, and classical methods for estimation using such data use carefully chosen imputation schemes for missing data, or likelihood approximations that lead to nonconvex $M$-estimation problems. These methods and their relatives are suitable for sc...
We study the robustness of the $F$-test in random design linear models, and reach a somewhat nuanced conclusion. On the positive side, one of our main results is that the size of the test is close to its nominal level as soon as either the distribution of the normalised error vector is close to uniform on the unit sphe...
A general framework is developed to quantify the extent to which any ensembling strategy defined via averaging can yield stability guarantees for any type of data perturbation, and provides much sharper guarantees than those obtained from privacy considerations.
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