New Fisher Widths Analyze Statistical Manifold Complexity

This paper introduces two new functionals, the primal Fisher width and the inverse-Fisher width, to analyze Gaussian-width complexity on statistical manifolds. These widths offer complementary insights into local parameter fluctuations and anisotropic Gaussian measurements, respectively. The research establishes a sharp relationship between these two widths, demonstrating that Fisher anisotropy cannot simultaneously reduce both relative to the Euclidean scale. AI

IMPACT Introduces new theoretical frameworks for analyzing statistical manifolds, potentially impacting future machine learning model development.

RANK_REASON The item is an academic paper published on arXiv detailing new theoretical concepts in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• arXiv
• Euclidean
• Fisher balls
• Fisher information
• Fisher Metric for Diagonalizable Quadratic Hamiltonians and Application to Phase Transitions
• Fisher spectrum
• Fisher Widths
• Gaussian Width
• inverse Fisher metric
• statistical manifolds

• paper
• other

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