Estimating the expected output of wide random MLPs more efficiently than sampling
arXiv:2605.05179v1 Announce Type: cross
Abstract: By far the most common way to estimate an expected loss in machine learning is to draw samples, compute the loss on each one, and take the empirical average. However, sampling is not necessarily optima…