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. Despite its prevalent use, the theoretical foundation of Adam remains largely unexplored for time-varying and nonstationary systems. In fact, the existing theoretical analyses of Adam-type algorithms are primarily concerned with time-invariant model parameters and explicitly or implicitly rely on independent and identically distributed (i.i.d.) data assumptions, under which
The proliferation of complex, dynamic AI systems necessitates deeper theoretical understanding of fundamental optimization algorithms like Adam to improve their stability and efficiency.
Improved theoretical understanding of Adam's behavior in non-ideal conditions can lead to more robust, reliable, and performant AI models, impacting a wide range of applications from real-time control to large language models.
This research provides a foundational step towards optimizing Adam-type algorithms for systems with time-varying parameters and non-i.i.d. data, moving beyond current theoretical limitations.
- · AI researchers
- · Machine learning developers
- · Industries relying on dynamic AI systems
- · Developers using Adam without deep theoretical understanding
More stable and efficient training of deep learning models in complex, real-world environments.
Accelerated development and deployment of AI agents and autonomous systems due to enhanced algorithmic reliability.
Potentially enables new classes of AI applications that require high adaptability and robustness in non-stationary settings.
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Read at arXiv cs.LG