arXiv:2512.05337v2 Announce Type: replace-cross Abstract: We consider the problem of learning the parameters of a $N$-dimensional stochastic linear dynamics under both full and partial observations from a single trajectory of time $T$. We introduce and analyze a new estimator that achieves a small maximum element-wise error on the recovery of symmetric dynamic matrices using only $T=\mathcal{O}(\log N)$ observations, irrespective of whether the matrix is sparse or dense. This estimator is based on the method of moments and does not rely on problem-specific regularization. This is especially im
The continuous drive for more efficient and robust machine learning algorithms, particularly for complex dynamical systems, propels this research forward.
This breakthrough in learning dynamics from minimal data could significantly accelerate the development and deployment of autonomous systems and advanced AI agents.
The ability to accurately learn system parameters from very few observations reduces data requirements and computational costs for complex system modeling and control.
- · AI agents developers
- · Robotics industry
- · Autonomous systems designers
- · Machine learning researchers
- · Traditional high-data system identification methods
- · Legacy control system design relying on extensive calibration
More robust and data-efficient AI models can be trained for dynamic environments.
Accelerated development of complex adaptive systems that learn quickly in real-world scenarios with limited data.
Enhanced AI autonomy across various applications, from industrial control to smart infrastructure, due to rapid model adaptation.
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