Stability analysis of stochastic gradient descent for homogeneous neural networks and linear classifiers

We prove new generalization bounds for stochastic gradient descent when training classifiers with invariances. Our analysis is based on the stability framework and covers both the convex case of linear classifiers and the non-convex case of homogeneous neural networks. We analyze stability with respect to the normalized version of the loss function used for training. This leads to investigating a form of angle-wise stability instead of euclidean stability in weights. For neural networks, the measure of distance we consider is invariant to rescaling the weights of each layer. Furthermore, we exploit the notion of on-average stability in order to obtain a data-dependent quantity in the bound. This data-dependent quantity is seen to be more favorable when training with larger learning rates in our numerical experiments. This might help to shed some light on why larger learning rates can lead to better generalization in some practical scenarios.(c) 2023 Elsevier Ltd. All rights reserved.

Automated summary

We provide new generalization bounds for stochastic gradient descent in training classifiers with invariances. Our analysis covers both convex and non-convex cases and is based on the stability framework. We investigate angle-wise stability instead of euclidean stability in weights for training and consider an invariant distance measure for neural networks. Moreover, we utilize on-average stability to obtain a data-dependent quantity in the bound, which proves to be more favorable with larger learning rates in our experiments.