arXiv Machine Learning July 14, 2026 By Zhankun Luo, Antesh Upadhyay, Sang Bin Moon, Abolfazl Hashemi

First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints

Read the original on arXiv Machine Learning →

arXiv:2603. 05774v2 Announce Type: replace Abstract: This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints.

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reinforcement-learning

arXiv Machine Learning

Sep 14

High-Probability Convergence of SGD via Batched Updates

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.

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arXiv Machine Learning

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FedeRage: Provably Convergent Agnostic Federated Learning under General Client Drift

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benchmarks safety

arXiv Machine Learning

4d ago

High-Probability Convergence of Clipped SGD under Heavy-Tailed Noise and $(L_0,L_1)$-Smoothness

arXiv:2505.20817v3 Announce Type: replace-cross Abstract: Gradient clipping is widely used in language-model training to control heavy-tailed gradient noise and can improve convergence guarantees ove...

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arXiv Machine Learning

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arXiv AI

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efficiency benchmarks

arXiv AI

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The Role of Gradient Modification in Heavy-Tailed Nonconvex Stochastic Min-Max Optimization

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By Tianxi Zhu, Yi Xu, Xiangyang Ji

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