Dimension independent excess risk by stochastic gradient descent
One classical canon of statistics is that large models are prone to overfitting, and model
selection procedures are necessary for high dimensional data. However, many …
selection procedures are necessary for high dimensional data. However, many …
Non-splitting Neyman-Pearson Classifiers
The Neyman-Pearson (NP) binary classification paradigm constrains the more severe type
of error (eg, the type I error) under a preferred level while minimizing the other (eg, the type II …
of error (eg, the type I error) under a preferred level while minimizing the other (eg, the type II …