Reviews: Early Stopping for Nonparametric Testing

Summary: This is a very interesting paper that provides an alternative approach to nonparametric testing. Instead of imposing a penalty to encourage bias-variance tradeoff, the paper proposed to do an "early stopping" to achieve that. The reason why this approach would work is that when applying a gradient ascent/descent approach, even the optimal solution will overfit the data, on the way to the optimal, there is a sweet spot where the variability has been removed a lot while the bias is still not too large (have not yet overfit the data). Overall, this is a nice and well-written paper and its idea is worth spreading in the community of machine learning and statistics. In Theorem 3.1 and soon after it, there is a rule for testing H0 using the asymptotic distribution.