To realize this possibility, we observe that a neural network classifier with N parameters can be interpreted as a weighted ensemble of N classifiers, and that in the lazy regime limit these classifiers are fixed throughout learning.

Tweedie's formula, we show that we can perform gradient steps to sequentially optimize these objectives.

Most applications of machine learning to classification assume a closed set of balanced classes.

However, existing frameworks face several interrelated limitations.