Towards Stability of Parameter-free Optimization
<|reference_start|>Towards Stability of Parameter-free Optimization: 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.<|reference_end|>
@article{pang2024towards,
    title={Towards Stability of Parameter-free Optimization},
    author={Yijiang Pang, Shuyang Yu, Bao Hoang, Jiayu Zhou},
    journal={arXiv preprint arXiv:2405.04376},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04376},
    primaryClass={cs.LG}
}