The implicit bias of gradient descent on nonseparable data

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Ziwei Ji, Matus Telgarsky ;
Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1772-1798, 2019.

Abstract

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 $\cO(\nicefrac{\ln\ln t }{\ln t})$. 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 $\cO(\nicefrac{(\ln t)^2}{\sqrt{t}})$.

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