Characterization of Excess Risk for Locally Strongly Convex Population Risk

TL;DR

This paper derives bounds on the excess risk of models trained with iterative algorithms under local strong convexity of the population risk, showing good generalization even in high-dimensional non-convex settings.

Contribution

It introduces dimension-insensitive excess risk bounds based on local convexity, applicable to a broad class of algorithms and non-convex problems.

Findings

Bound of order (1/n) for convex problems.

Order (1/n) for certain non-convex problems with no spurious minima.

Bound of order (1/(n)) without no spurious minima assumption.

Abstract

We establish upper bounds for the expected excess risk of models trained by proper iterative algorithms which approximate the local minima. Unlike the results built upon the strong globally strongly convexity or global growth conditions e.g., PL-inequality, we only require the population risk to be \emph{locally} strongly convex around its local minima. Concretely, our bound under convex problems is of order $\tilde{\cO}(1/n)$. For non-convex problems with $d$ model parameters such that $d/n$ is smaller than a threshold independent of $n$, the order of $\tilde{\cO}(1/n)$ can be maintained if the empirical risk has no spurious local minima with high probability. Moreover, the bound for non-convex problem becomes $\tilde{\cO}(1/\sqrt{n})$ without such assumption. Our results are derived via algorithmic stability and characterization of the empirical risk's landscape. Compared with the existing algorithmic stability based results, our bounds are dimensional insensitive and without restrictions on the algorithm's implementation, learning rate, and the number of iterations. Our bounds underscore that with locally strongly convex population risk, the models trained by any proper iterative algorithm can generalize well, even for non-convex problems, and $d$ is large.