An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
Title resolution pending
5 Pith papers cite this work, alongside 563 external citations. Polarity classification is still indexing.
5
Pith papers citing it
563
external citations · Crossref
citation-role summary
background 1
citation-polarity summary
years
2026 5roles
background 1polarities
background 1representative citing papers
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
SHIELD dataset and distilled DeBERTa v3 model achieve 0.88 micro precision and 0.86 recall on PHI de-identification while matching teacher performance on structured categories.
The paper delivers a chronological history of Fréchet distances connecting early abstract set theory to curve metrics, optimal transport, and the FID metric in generative models.
citing papers explorer
-
Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
-
Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
-
Scale selection for geometric medians on product manifolds
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
-
SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification
SHIELD dataset and distilled DeBERTa v3 model achieve 0.88 micro precision and 0.86 recall on PHI de-identification while matching teacher performance on structured categories.
-
A Brief History of Fr\'echet Distances: From Curves and Probability Laws to FID
The paper delivers a chronological history of Fréchet distances connecting early abstract set theory to curve metrics, optimal transport, and the FID metric in generative models.