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New PAC-Bayesian Bounds Developed for Partially Observed LTI Systems

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Researchers have developed new PAC-Bayesian error bounds for partially observed stochastic linear time-invariant state-space systems that include inputs and sub-Gaussian noise. These bounds connect the expected prediction errors to the errors made by the model on its training data, and can also be used to derive bounds for parameter estimation errors. This work could serve as a foundational step towards establishing PAC-Bayesian bounds for recurrent neural networks, given that linear time-invariant systems are a subset of RNNs. AI

IMPACT Establishes theoretical bounds for system identification, potentially advancing the understanding and development of recurrent neural networks.

RANK_REASON The cluster contains a single academic paper detailing a new theoretical framework in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PAC-Bayesian Bounds Developed for Partially Observed LTI Systems

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The cluster contains a single academic paper detailing a new theoretical framework in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mihaly Petreczky, Mohamad Al Ahdab, John Leth ·

    PAC-Bayesian Bounds for Learning Partially Observed Stochastic Linear Time-Invariant State-Space Systems with Inputs and Sub-Gaussian Noise

    arXiv:2609.08740v1 Announce Type: new Abstract: In this paper we derive a Probably Approximately Correct (PAC)-Bayesian error bound for partially observed linear time-invariant (LTI) stochastic dynamical systems in state-space form with inputs and sub-Gaussian noise. Such bounds …

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