Hyperparameter Optimization (HPO) can lift the burden of tuning hyperparameters (HPs) of neural networks. HPO algorithms from the Population Based Training (PBT) family are efficient thanks to dynamically adjusting HPs every few steps of the weight optimization. Recent results indicate that the number of steps between HP updates is an important meta-HP of all PBT variants that can substantially affect their performance. Yet, no method or intuition is available for efficiently setting its value. We introduce Iterated Population Based Training (IPBT), a novel PBT variant that automatically adjusts this HP via restarts that reuse weight information in a task-agnostic way and leverage time-varying Bayesian optimization to reinitialize HPs. Evaluation on 8 image classification and reinforcement learning tasks shows that, on average, our algorithm matches or outperforms 5 previous PBT variants and other HPO algorithms (random search, ASHA, SMAC3), without requiring a budget increase or any changes to its HPs. The source code is available at https://github.com/AwesomeLemon/IPBT.

翻译：超参数优化方法能够减轻神经网络超参数调优的负担。基于人口训练（PBT）家族的优化算法通过在权重优化过程中每隔若干步动态调整超参数，展现出高效性。最新研究表明，超参数更新间隔步数是所有PBT变体的关键元超参数，其取值会显著影响算法性能。然而，目前缺乏有效设置该参数值的系统方法或直觉依据。本文提出迭代式人口训练（IPBT）这一新型PBT变体，通过任务无关方式复用权重信息进行重启，并利用时变贝叶斯优化重新初始化超参数，从而自动调整该超参数。在8项图像分类与强化学习任务上的评估表明：在不增加计算预算且无需修改算法超参数的前提下，本方法平均性能持平或超越5种既有PBT变体及其他超参数优化算法（随机搜索、ASHA、SMAC3）。源代码已开源至https://github.com/AwesomeLemon/IPBT。