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English(EN) Learning Ergodic Dynamical Systems from a Finite Trajectory

两篇arXiv论文详述从单条轨迹学习动力学系统 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新提交至arXiv的stat.ML板块的研究论文探讨了从单条轨迹学习动力学系统。第一篇论文侧重于切换非线性动力学系统，基于函数类熵为预测风险提供理论保证，并获得明确的收敛速率。第二篇论文处理遍历动力学系统，通过非线性最小二乘法推导出预测函数估计的高概率保证，并将框架扩展到高阶系统和Koopman算子。 AI

影响 这些论文推进了动力学系统机器学习的理论理解，可能能够对复杂时间序列数据进行更鲁棒的分析。

排序理由 两篇发表在arXiv上的学术论文，详细介绍了动力学系统机器学习的新研究。

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

两篇arXiv论文详述从单条轨迹学习动力学系统 · 跟踪2个来源

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两篇发表在arXiv上的学术论文，详细介绍了动力学系统机器学习的新研究。
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报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Sunny G. W. Wang, Hemant Tyagi ·

    从单轨迹学习切换非线性动力学系统

    arXiv:2607.23502v1 Announce Type: new Abstract: We study empirical risk minimization for learning non-linear dynamical systems whose transition dynamics may switch over time. Under stability assumptions, and i.i.d switching over a set of $K$ modes, we derive non-asymptotic bounds…

  2. arXiv stat.ML TIER_1 English(EN) · Oleksii Kachaiev, Silvia Villa, Lorenzo Rosasco ·

    从有限轨迹学习遍历动力学系统

    arXiv:2607.22399v1 Announce Type: new Abstract: We consider the problem of learning from a single finite trajectory of an ergodic stochastic dynamical system. More precisely, we study discrete-time autonomous stochastic systems defining time-homogeneous Markov processes. We first…

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