arXiv Machine Learning By Laurent Caraffa

The Cost Geometry of Belief: finite-resource inference under noisy observation

Read the original on arXiv Machine Learning →

arXiv:2606. 21585v2 Announce Type: replace Abstract: A finite machine's digital twin of a system observes the territory through finite, noisy sensors; we model its coherent output as a belief, a probability density over states, the Bayes posterior, never a point.

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arXiv Machine Learning
Jun 30

A Transport-Based Geometry of Belief-Cost

arXiv:2606. 21585v4 Announce Type: replace Abstract: A finite agent, a machine's digital twin or any bounded reasoner, infers a fixed and noisy world through finite sensors, so its coherent output is a belief: a probability density over states (the Bayes posterior).

By Laurent Caraffa
arXiv Machine Learning
Jul 24

Fisher Widths: Local Learning Geometry and Anisotropic Recovery

arXiv:2607. 20578v1 Announce Type: new Abstract: We study Gaussian-width complexity on statistical manifolds through a pair of functionals: the primal Fisher width $w_G(T) = w(G^{1/2}T)$, induced by the Fisher metric, and the inverse-Fisher width $w_{G^{-1}}(T) = w(G^{-1/2}T)$, induced by the inverse Fisher metric.

By Vu Khac Ky