MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters
Keywords: Optimization, Automatic step-size optimization, Automatic hyperparameter optimization, Continual learning
TL;DR: We introduce MetaOptimize framework that dynamically adjusts meta-parameters, particularly step sizes (also known as learning rates), during training; moving away from the computationally expensive traditional meta-parameter search methods.
Abstract: We address the challenge of optimizing meta-parameters (i.e., hyperparameters) in machine learning algorithms, a critical factor influencing training efficiency and model performance. Moving away from the computationally expensive traditional meta-parameter search methods, we introduce MetaOptimize framework that dynamically adjusts meta-parameters, particularly step sizes (also known as learning rates), during training. More specifically, MetaOptimize can wrap around any first-order optimization algorithm, tuning step sizes on the fly to minimize a specific form of regret that accounts for long-term effect of step sizes on training, through a discounted sum of future losses. We also introduce low complexity variants of MetaOptimize that, in conjunction with its adaptability to multiple optimization algorithms, demonstrate performance competitive to those of best hand-crafted learning-rate schedules across various machine learning applications.
Primary Area: optimization
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Submission Number: 12451
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