arXiv:2402.11215v4 Announce Type: replace
Abstract: The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-sc...
By Tim Tsz-Kit Lau, Han Liu, Mladen Kolar
arXiv:2607. 16261v1 Announce Type: cross Abstract: Modern optimizers combine gradients from the current mini-batch with historical optimization state, such as momentum or adaptive moments.
By Apostolos Avranas
arXiv:2606. 27171v1 Announce Type: new Abstract: This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization.
By Jonne Pohjankukka, Jukka Heikkonen
arXiv:2607. 16261v2 Announce Type: replace-cross Abstract: Modern optimizers combine gradients from the current mini-batch with historical optimization state, such as momentum or adaptive moments.
By Apostolos Avranas
The paper introduces SHANG++—an accelerated stochastic gradient descent algorithm designed to be robust under multiplicative noise scaling (MNS). Building on a semi‑implicit discretization called SHANG, SHANG++ adds a damping correction that improves stability and convergence for both convex and strongly convex objectives. Experiments on convex problems and deep learning tasks, including a noise‑robust test on ResNet‑34, show that SHANG++ consistently outperforms existing accelerated methods with minimal parameter sensitivity.
By Yaxin Yu, Long Chen, Minfu Feng
The paper introduces Batched SGD, a variant that groups online samples into epochs and performs a single update per epoch using a low‑variance gradient estimate. This batching approach allows a straightforward high‑probability analysis without restrictive assumptions or auxiliary sequences, yielding near‑optimal rates for both strongly convex and non‑convex objectives under standard smoothness and sub‑Gaussian noise conditions. The authors also extend the method to federated learning, providing the first high‑probability guarantees with logarithmic communication complexity, linear speedup in the number of agents, and robustness to data heterogeneity.
By Feng Zhu, Robert W. Heath Jr., Aritra Mitra