Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
Low distortion delaunay embedding of trees in hyperbolic plane
9 Pith papers cite this work. Polarity classification is still indexing.
9
Pith papers citing it
citation-role summary
background 1
method 1
citation-polarity summary
years
2026 9verdicts
UNVERDICTED 9representative citing papers
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.
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.
SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.
SLoD detects emergent scale boundaries in knowledge graphs by applying spectral heat diffusion to Poincare embeddings, recovering planted hierarchies in synthetic data and aligning with taxonomic depths in WordNet without resolution-parameter tuning.
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.
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
A fully hyperbolic attention model using Busemann energy in Poincaré discs produces emotion predictions from text that generalize well even at low embedding dimensions.
A kernel-based regularized learning framework for FDR control that unifies arbitrary structures and supplies provably valid decision rules with likelihood-based tuning.
citing papers explorer
-
Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
-
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.
-
Rates of forgetting for the sequentially Markov coalescent
SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.
-
Semantic Level of Detail for Knowledge Graphs: Discovering Abstraction Boundaries via Spectral Heat Diffusion
SLoD detects emergent scale boundaries in knowledge graphs by applying spectral heat diffusion to Poincare embeddings, recovering planted hierarchies in synthetic data and aligning with taxonomic depths in WordNet without resolution-parameter tuning.
-
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.
-
HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
-
Busemann energy-based attention for emotion analysis in Poincar\'e discs
A fully hyperbolic attention model using Busemann energy in Poincaré discs produces emotion predictions from text that generalize well even at low embedding dimensions.
-
Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels
A kernel-based regularized learning framework for FDR control that unifies arbitrary structures and supplies provably valid decision rules with likelihood-based tuning.