Adaptive Learning Rates with Maximum Variation Averaging

Adaptive gradient methods such as RMSProp and Adam use an exponential moving estimate of the squared gradient to compute coordinate-wise adaptive step sizes, achieving better convergence than SGD in face of noisy objectives. However, Adam can have undesirable convergence behavior due to unstable or extreme adaptive learning rates. Methods such as AMSGrad and AdaBound have been proposed to stabilize the adaptive learning rates of Adam in the later stage of training, but they do not outperform Adam in some practical tasks such as training Transformers. In this paper, we propose an adaptive learning rate principle, in which the running mean of the squared gradient is replaced by a weighted mean, with weights were chosen to maximize the estimated variance of each coordinate. This gives a worst-case estimate for the local gradient variance, taking smaller steps when large curvatures or noisy gradients are present, which leads to more desirable convergence behavior than Adam. We prove the proposed algorithm converges under mild assumptions for nonconvex stochastic optimization problems, and demonstrate the improved efficacy of our adaptive averaging approach on image classification, machine translation, and natural language understanding tasks. Moreover, our method overcomes the non-convergence issue of Adam in BERT pretraining at large batch sizes, while achieving better test performance than LAMB in the same setting.