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Two arXiv papers detail learning dynamical systems from single trajectories · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two new research papers submitted to arXiv's stat.ML section explore the learning of dynamical systems from single trajectories. The first paper focuses on switched non-linear dynamical systems, providing theoretical guarantees for prediction risk based on function class entropy and obtaining explicit convergence rates. The second paper addresses ergodic dynamical systems, deriving high-probability guarantees for estimating prediction functions using non-linear least squares and extending the framework to higher-order systems and Koopman operators. AI

IMPACT These papers advance theoretical understanding in machine learning for dynamical systems, potentially enabling more robust analysis of complex time-series data.

RANK_REASON Two academic papers published on arXiv detailing new research in machine learning for dynamical systems.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Two arXiv papers detail learning dynamical systems from single trajectories · 2 sources tracked

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Two academic papers published on arXiv detailing new research in machine learning for dynamical systems.
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COVERAGE [2]

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

    Learning switched non-linear dynamical systems from a single trajectory

    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 ·

    Learning Ergodic Dynamical Systems from a Finite Trajectory

    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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