LALR：回归和神经网络中Lipschitz自适应学习率的理论和实验验证
LALR: Theoretical and Experimental validation of Lipschitz Adaptive Learning Rate in Regression and Neural Networks
摘要：
我们提出了一个理论框架的平均绝对误差损失函数和分位数损失函数的自适应学习率政策，并评估其有效性回归任务。该框架基于Lipschitz连续性理论，特别是利用损失函数的学习率和Lipschitz常数之间的关系。基于实验，我们发现自适应学习率策略与恒定学习率策略相比，收敛速度快20倍。
英文摘要：
We propose a theoretical framework for an adaptive learning rate policy for the Mean Absolute Error loss function and Quantile loss function and evaluate its effectiveness for regression tasks. The framework is based on the theory of Lipschitz continuity, specifically utilizing the relationship between learning rate and Lipschitz constant of the loss function. Based on experimentation, we have found that the adaptive learning rate policy enables up to 20x faster convergence compared to a constant learning rate policy.
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