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

为带部分观测的LTI系统开发了新的PAC-贝叶斯界

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员为包含输入和亚高斯噪声的带部分观测的随机线性时不变状态空间系统开发了新的PAC-贝叶斯误差界。这些界将预期预测误差与模型在其训练数据上产生的误差联系起来，还可以用于推导参数估计误差的界。鉴于线性时不变系统是RNN的子集，这项工作可以作为建立循环神经网络PAC-贝叶斯界的基础步骤。 AI

影响为系统识别建立了理论界，可能促进循环神经网络的理解和发展。

排序理由该集群包含一篇详细介绍机器学习新理论框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Mihaly Petreczky, Mohamad Al Ahdab, John Leth · 2026-09-10 04:00

具有输入和亚高斯噪声的带部分观测的随机线性时不变状态空间系统的PAC-贝叶斯界

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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具有输入和亚高斯噪声的带部分观测的随机线性时不变状态空间系统的PAC-贝叶斯界

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