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New algorithm tackles online learning of complex linear dynamical systems

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new unified algorithm for online learning of Linear Dynamical Systems (LDS). This algorithm aims to achieve sublinear regret while adapting its memory usage to the intrinsic complexity of the system's dynamics, rather than the full hidden state dimension. The focus is on systems with low instability complexity, where stabilization is plausible. The proposed algorithm handles all LDS, including non-diagonalizable systems, with a learnable parameter count of $\widetilde{O}(k)$, where $k$ represents the instability complexity. A theoretical lower bound confirms that $k$ is a valid complexity measure, and experimental results show superior performance compared to prior methods. AI

IMPACT This research could lead to more efficient methods for controlling and predicting complex dynamic systems, potentially impacting areas like robotics and control theory.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for a specific machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New algorithm tackles online learning of complex linear dynamical systems

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The cluster contains an academic paper detailing a new algorithm for a specific machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yuval Ran-Milo, Angelos Assos, Elad Hazan ·

    A Memory Efficient Unified Algorithm for Online Learning of Linear Dynamical Systems

    arXiv:2607.02050v1 Announce Type: new Abstract: Motivated by the challenge of stabilizing a general unknown linear dynamical system (LDS) from observations, we study the natural prerequisite of online prediction. Our goal is to achieve sublinear regret with a memory footprint tha…

  2. arXiv cs.LG TIER_1 English(EN) · Elad Hazan ·

    A Memory Efficient Unified Algorithm for Online Learning of Linear Dynamical Systems

    Motivated by the challenge of stabilizing a general unknown linear dynamical system (LDS) from observations, we study the natural prerequisite of online prediction. Our goal is to achieve sublinear regret with a memory footprint that adapts to the intrinsic complexity of the dyna…

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