Towards Stability of Parameter-free Optimization

• URL: http://arxiv.org/abs/2405.04376v3
• Date: Mon, 27 May 2024 14:46:21 GMT
• Title: Towards Stability of Parameter-free Optimization
• Authors: Yijiang Pang, Shuyang Yu, Bao Hoang, Jiayu Zhou,
• Abstract summary: We propose a novel parameter-free gradient, textscAdamG (Adam with the golden step size) textscAdamG achieves superior performance, which is consistently on par with Adam using a manually tuned learning rate.
• Score: 28.012355508745543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hyperparameter tuning, particularly the selection of an appropriate learning rate in adaptive gradient training methods, remains a challenge. To tackle this challenge, in this paper, we propose a novel parameter-free optimizer, \textsc{AdamG} (Adam with the golden step size), designed to automatically adapt to diverse optimization problems without manual tuning. The core technique underlying \textsc{AdamG} is our golden step size derived for the AdaGrad-Norm algorithm, which is expected to help AdaGrad-Norm preserve the tuning-free convergence and approximate the optimal step size in expectation w.r.t. various optimization scenarios. To better evaluate tuning-free performance, we propose a novel evaluation criterion, \textit{reliability}, to comprehensively assess the efficacy of parameter-free optimizers in addition to classical performance criteria. Empirical results demonstrate that compared with other parameter-free baselines, \textsc{AdamG} achieves superior performance, which is consistently on par with Adam using a manually tuned learning rate across various optimization tasks.

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