Using Statistics to Automate Stochastic Optimization

NeurIPS 2019 Hunter LangPengchuan ZhangLin Xiao

Despite the development of numerous adaptive optimizers, tuning the learning rate of stochastic gradient methods remains a major roadblock to obtaining good practical performance in machine learning. Rather than changing the learning rate at each iteration, we propose an approach that automates the most common hand-tuning heuristic: use a constant learning rate until "progress stops," then drop... (read more)

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