A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization

• URL: http://arxiv.org/abs/2102.10880v1
• Date: Mon, 22 Feb 2021 10:26:31 GMT
• Title: A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
• Authors: Filip de Roos, Carl Jidling, Adrian Wills, Thomas Sch\"on and Philipp Hennig
• Abstract summary: We provide a probabilistic motivation, in terms of Gaussian inference, for popular first-order methods. The inference allows us to relate the learning rate to a dimensionless quantity that can be automatically adapted during training. The resulting meta-algorithm is shown to adapt learning rates in a robust manner across a large range of initial values.
• Score: 20.77923050735746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning practitioners invest significant manual and computational resources in finding suitable learning rates for optimization algorithms. We provide a probabilistic motivation, in terms of Gaussian inference, for popular stochastic first-order methods. As an important special case, it recovers the Polyak step with a general metric. The inference allows us to relate the learning rate to a dimensionless quantity that can be automatically adapted during training by a control algorithm. The resulting meta-algorithm is shown to adapt learning rates in a robust manner across a large range of initial values when applied to deep learning benchmark problems.

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