自适应采样提升Adam优化器收敛速度
ADAM Optimization with Adaptive Batch Selection
- 用组合强化学习动态选样本，避免均匀采样偏差
- 理论证明收敛速度优于传统Adam及同类方法
- 适合追求高效训练的深度学习研究者
Adam因其自适应学习率被广泛用于神经网络训练。但不同数据样本对模型更新的影响程度不一，等权采样会导致收敛效率低下。此前工作采用老虎机框架调整采样分布以实现自适应采样，但缺乏充分的理论保证。本文提出结合组合老虎机技术的AdamCB优化器，能同时利用多个样本的反馈，显著增强理论可证性与实际性能。遗憾分析表明，AdamCB的累积损失增长速率低于现有基于老虎机的Adam变体。数值实验显示，AdamCB在多种任务中持续优于已有方法。
原文摘要 · Abstract (English)
Adam is a widely used optimizer in neural network training due to its adaptive learning rate. However, because different data samples influence model updates to varying degrees, treating them equally can lead to inefficient convergence. To address this, a prior work proposed adapting the sampling distribution using a bandit framework to select samples adaptively. While promising, the bandit-based variant of Adam suffers from limited theoretical guarantees. In this paper, we introduce Adam with Combinatorial Bandit Sampling (AdamCB), which integrates combinatorial bandit techniques into Adam to resolve these issues. AdamCB is able to fully utilize feedback from multiple samples at once, enhancing both theoretical guarantees and practical performance. Our regret analysis shows that AdamCB achieves faster convergence than Adam-based methods including the previous bandit-based variant. Numerical experiments demonstrate that AdamCB consistently outperforms existing methods.
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