L(w; x; y) be a convex loss function for a linear classifier
w. Can you always express the gradient of
f(y; wx)*x? I.e, is the gradient always some scalar function
f of the gold label
y and the prediction
x? This seems to hold for hinge loss, square loss and logistic loss.