Geometric Moment Contraction for Stochastic Nesterov Acceleration
stat.ML, cs.LG
Submitted: 2026-09-25
Updated: 2026-09-25
Project page: https://jiaqili97.github.io/research.html
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- A Polyak-Ruppert Central Limit Theorem for SA-Adam with Momentum and Non-Convergent Adaptive Preconditioning
- Limit Theorems for Stochastic Gradient Descent with Infinite Variance
- Statistical Inference for Stochastic Gradient Descent: Beyond Finite Variance
- Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy-Tailed Noise
- The Anytime Convergence of Stochastic Gradient Descent with Momentum: From a Continuous-Time Perspective
- Strong error analysis for the stochastic momentum optimizer
- Nesterov acceleration in benignly non-convex landscapes
- Stochastic Gradient Descent with Momentum is Algorithmically Stable
- Revisiting the central limit theorems for the SGD-type methods
- Convergence and concentration properties of constant step-size SGD through Markov chains
- Acceleration of stochastic gradient descent with momentum by averaging: finite-sample rates and asymptotic normality
- Generalized Stochastic Gradient Descent with Momentum Methods for Smooth Optimization
- Unified Convergence Analysis of Stochastic Momentum Methods for Convex and Non-convex Optimization
- SHANG++: Robust Stochastic Acceleration under Multiplicative Noise
- Delayed supermartingale convergence lemmas for stochastic approximation with Nesterov momentum
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