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A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning

arXiv 2409.06437 ↗First posted 10 Sep 2024cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

In this note, we give a short information-theoretic proof of the consistency of the Gaussian maximum likelihood estimator in linear auto-regressive models. Our proof yields nearly optimal non-asymptotic rates for parameter recovery and works without any invocation of stability in the case of finite hypothesis classes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 7 references that are in the map. First posted on arXiv 10 Sep 2024.

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References · 4

  1. 2017Information-theoretic analysis of generalization capability of learning algorithmscited by 303
  2. 2018Learning Without Mixing: Towards A Sharp Analysis of Linear System Identificationcited by 158
  3. 2024Sharp Rates in Dependent Learning Theory: Avoiding Sample Size Deflation for the Square Losscited by 10
  4. 2024Information-Theoretic Foundations for Machine Learningcited by 2

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