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English(EN) Random tilts to find stationary points in stochastic convex optimization

四篇arXiv论文推进随机优化理论 · 跟踪4个来源

作者 PulseAugur 编辑部 · [5 个来源] ·

四篇新研究论文发表在arXiv上，探讨了随机优化方法的高级收敛性质。第一篇论文为具有收缩性、马尔可夫性和乘性噪声的随机逼近的稳态收敛性引入了统一理论，在Wasserstein-2距离上实现了最优的$O(\sqrt{\alpha})$速率。另一篇论文证明了带裁剪和加性高斯噪声的随机梯度下降的几乎处处收敛性，并将其扩展到动量变体。第三项研究分析了重尾噪声和Hölder平滑下SGD的收敛速率，为具有挑战性条件下的梯度裁剪方法提供了新的保证。最后，第四篇论文提出了一种使用随机倾斜寻找随机凸优化中驻点的方法，实现了$\sqrt{d/n}$的驻点残差阶。 AI

影响 推进了对对训练大规模AI模型至关重要的优化算法的理论理解。

排序理由 该集群由四篇关于理论机器学习主题的学术论文组成，发表在arXiv上。

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四篇arXiv论文推进随机优化理论 · 跟踪4个来源

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该集群由四篇关于理论机器学习主题的学术论文组成，发表在arXiv上。
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报道来源 [5]

  1. arXiv cs.LG TIER_1 English(EN) · Yixuan Zhang, Qiaomin Xie ·

    随机逼近的稳态收敛

    arXiv:2609.14922v1 Announce Type: cross Abstract: For constant-stepsize stochastic approximation (SA), the iterates converge in distribution to a stationary law that depends on the stepsize $\alpha.$ Steady-state convergence (SSC) concerns the limit of the scaled stationary distr…

  2. arXiv cs.LG TIER_1 English(EN) · Amartya Mukherjee, Jun Liu ·

    带裁剪和加性噪声的随机梯度方法的近乎确定收敛性分析

    arXiv:2609.12119v1 Announce Type: new Abstract: Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, particularly in applications requiring robustness or privacy guarantees. However, clip…

  3. arXiv cs.LG TIER_1 English(EN) · Misbah Uz Zaman, Anirbit Mukherjee ·

    重尾噪声和Hölder光滑性下随机梯度方法的收敛性

    arXiv:2609.12785v1 Announce Type: new Abstract: Classical convergence guarantees for stochastic gradient methods typically assume Lipschitz-smooth objectives and finite-variance gradient noise, both frequently violated in practice. In contrast, we study nonconvex stochastic optim…

  4. arXiv stat.ML TIER_1 English(EN) · Djamel Rassem Lamouri, Dorian Baudry, Nicolas Gast ·

    常数步长下非线性两时间尺度随机逼近的偏差

    arXiv:2609.20409v1 Announce Type: cross Abstract: Two-timescale stochastic approximation (TTSA) is a fundamental tool for analyzing coupled iterative algorithms in reinforcement learning, optimization, and stochastic control. However, finite-time guarantees for nonlinear two-time…

  5. arXiv stat.ML TIER_1 English(EN) · Felipe Areces, John C. Duchi, Malo Sommers ·

    随机倾斜法寻找随机凸优化中的驻点

    arXiv:2609.17798v1 Announce Type: cross Abstract: We consider the problem of finding stationary points of stochastic convex functions and related variational inequalities. For each, we show that regularized empirical risk minimization, coupled with a random tilting perturbation, …

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