arXiv Machine Learning
Convergence rates for the RMSprop optimizer with full control of the hyperparameters
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arXiv Machine Learning
Jun 2
Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad
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.
arXiv Machine Learning
Jun 4
Tight Long-Term Tail Decay of (Clipped) SGD in Non-Convex Optimization
arXiv:2602. 05657v2 Announce Type: replace Abstract: The study of tail behaviour of SGD-induced processes has been attracting a lot of interest, due to offering strong guarantees with respect to individual runs of an algorithm.
arXiv Machine Learning
4d ago
Uniform a priori bounds and error analysis for the Adam stochastic gradient descent optimization method
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.
arXiv Machine Learning
Jul 7
Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks
arXiv:2607. 04233v1 Announce Type: cross Abstract: Gradient based optimization methods are nowadays the methods of choice for training deep neural networks (DNNs) in artificial intelligence (AI) systems.
arXiv Machine Learning
Aug 12
Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization
arXiv:2310. 15976v4 Announce Type: replace Abstract: signSGD is attractive in nonconvex optimization because it communicates sign-valued rather than full-precision gradients.
arXiv Machine Learning
Jun 30
Analysis of Adam Algorithms for Stochastic Dynamic Systems
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.