Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization

• URL: http://arxiv.org/abs/2303.16952v1
• Date: Wed, 29 Mar 2023 18:17:41 GMT
• Title: Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization
• Authors: Tanmay Gautam, Samuel Pfrommer, Somayeh Sojoudi
• Abstract summary: We propose a meta-learning framework in which the inner loop optimization step involves solving a differentiable convex optimization (DCO) We illustrate the theoretical appeal of this approach by showing that it enables one-step optimization of a family of linear least squares problems.
• Score: 13.043909705693249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conventional optimization methods in machine learning and controls rely heavily on first-order update rules. Selecting the right method and hyperparameters for a particular task often involves trial-and-error or practitioner intuition, motivating the field of meta-learning. We generalize a broad family of preexisting update rules by proposing a meta-learning framework in which the inner loop optimization step involves solving a differentiable convex optimization (DCO). We illustrate the theoretical appeal of this approach by showing that it enables one-step optimization of a family of linear least squares problems, given that the meta-learner has sufficient exposure to similar tasks. Various instantiations of the DCO update rule are compared to conventional optimizers on a range of illustrative experimental settings.

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