  Gradient descent, when applied to the task of logistic regression, outputs
iterates which are biased to follow a unique ray defined by the data. The
direction of this ray is the maximum margin predictor of a maximal linearly
separable subset of the data; the gradient descent iterates converge to this
ray in direction at the rate $\mathcal{O}(\ln\ln t / \ln t)$. The ray does not
pass through the origin in general, and its offset is the bounded global
optimum of the risk over the remaining data; gradient descent recovers this
offset at a rate $\mathcal{O}((\ln t)^2 / \sqrt{t})$.
