arXiv:2605.18694v2 Announce Type: replace-cross Abstract: Many tasks in modern machine learning are observed to involve heavy-tailed gradient noise during the optimization process. To manage this realistic and challenging setting, new mechanisms, such as gradient clipping and gradient normalization, have been introduced to ensure the convergence of first-order algorithms. However, adaptive gradient methods, a famous class of modern optimizers that includes popular $\mathtt{Adam}$ and $\mathtt{AdamW}$, often perform well even without any extra operations mentioned above. It is therefore natural
The paper addresses a current challenge in deep learning optimization, particularly the robustness of adaptive gradient methods like AdaGrad in environments with heavy-tailed noise, which is increasingly prevalent in modern machine learning tasks.
Improved understanding and theoretical guarantees for optimization algorithms under realistic noise conditions enhance the reliability and efficiency of AI training, directly impacting performance and resource utilization.
This research provides theoretical backing for the observed empirical success of adaptive gradient methods in challenging noise environments, potentially guiding algorithm design and selection for robust AI systems.
- · AI researchers and developers
- · Companies deploying AI models
- · Edge AI applications
- · Hardware providers with efficient model training
- · Developers solely relying on naive SGD in noisy environments
More stable and efficient training of deep learning models in real-world scenarios with less need for manual hyperparameter tuning.
Accelerated development and deployment of robust AI applications, especially in areas with inherently noisy data or computational environments.
Reduced compute costs and energy consumption for training sophisticated AI models, as optimization becomes more efficient and less prone to divergence.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG