arXiv Machine Learning By Xin Zheng, Yifei Jin, Lei Guo

Analysis of Adam Algorithms for Stochastic Dynamic Systems

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arXiv:2606. 28879v1 Announce Type: new Abstract: The adaptive moment estimation algorithm, known as Adam, is widely used in modern machine learning, owing to its low per-iteration complexity and strong empirical performance.

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arXiv Machine Learning
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Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization

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The paper establishes uniform a priori bounds for the Adam optimizer, enabling an unconditional error analysis for a broad class of strongly convex stochastic optimization problems. Prior analyses were conditional, assuming Adam remained bounded, whereas this work removes that assumption. The results provide a rigorous foundation for Adam’s performance in training deep neural networks and other convex optimization tasks.

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