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Generalization Bounds for Label Noise Stochastic Gradient Descent

Jung Eun Huh · Patrick Rebeschini

MR1 & MR2 - Number 103
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Fri 3 May 8 a.m. PDT — 8:30 a.m. PDT

Abstract: We develop generalization error bounds for stochastic gradient descent (SGD) with label noise in non-convex settings under uniform dissipativity and smoothness conditions. Under a suitable choice of semimetric, we establish a contraction in Wasserstein distance of the label noise stochastic gradient flow that depends polynomially on the parameter dimension $d$. Using the framework of algorithmic stability, we derive time-independent generalisation error bounds for the discretized algorithm with a constant learning rate. The error bound we achieve scales polynomially with $d$ and with the rate of $n^{-2/3}$, where $n$ is the sample size. This rate is better than the best-known rate of $n^{-1/2}$ established for stochastic gradient Langevin dynamics (SGLD)---which employs parameter-independent Gaussian noise---under similar conditions. Our analysis offers quantitative insights into the effect of label noise.

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