{"total":11,"items":[{"citing_arxiv_id":"2606.19148","ref_index":283,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Fast Computation of Free-Support Wasserstein Medians","primary_cat":"stat.CO","submitted_at":"2026-06-17T14:50:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00233","ref_index":241,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Density Evolution: A Multiscale View of Density Estimation","primary_cat":"math.ST","submitted_at":"2026-05-29T18:08:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20681","ref_index":118,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis","primary_cat":"stat.ME","submitted_at":"2026-05-20T03:48:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17559","ref_index":25,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels","primary_cat":"stat.ME","submitted_at":"2026-05-17T17:42:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A kernel-based regularized learning framework for FDR control that unifies arbitrary structures and supplies provably valid decision rules with likelihood-based tuning.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08001","ref_index":98,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Scale selection for geometric medians on product manifolds","primary_cat":"math.ST","submitted_at":"2026-05-08T16:57:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03266","ref_index":60,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy","primary_cat":"stat.ML","submitted_at":"2026-05-05T01:37:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00363","ref_index":50,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space","primary_cat":"math.ST","submitted_at":"2026-05-01T02:54:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21027","ref_index":160,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering","primary_cat":"cs.AI","submitted_at":"2026-04-22T19:18:36+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A 22M-parameter hyperbolic model answers structured EHR questions with accuracy close to LLM-based systems (EHRXQA 89.5%, MIMIC-Instr 76.0%).","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"mov, 1987; Bridson and Haefliger, 1999), (C, dT ) is0-hyperbolic. (2) Results on embeddings of tree metrics into hyperbolic space show that any finite tree (C, dT ) admits, for everyε >0 , a (1+ε)-bilipschitz embed- ding into the hyperbolic planeH2; see, for example, Sarkar's construction of low-distortion Delaunay embeddings of trees in the hyperbolic plane (Sarkar, 2012). Concretely, there exists ψ:C →H 2 such that for allu, v∈ C, quasi-isometry (24) holds. The Lorentz hyperboloid model Hd L is isometric to other standard models of hyperbolic space (such as the Poincaré ball and half-space models) via smooth bijections that preserve geodesic distance (Bridson and Haefliger, 1999; Nickel and Kiela, 2017; Ganea et al."},{"citing_arxiv_id":"2604.20629","ref_index":90,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Rates of forgetting for the sequentially Markov coalescent","primary_cat":"math.PR","submitted_at":"2026-04-22T14:44:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"SMC forgets its initial condition geometrically in the jump chain and as 1/ℓ in continuous genetic distance, justifying independent-locus approximations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06752","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Busemann energy-based attention for emotion analysis in Poincar\\'e discs","primary_cat":"cs.LG","submitted_at":"2026-04-08T07:19:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A fully hyperbolic attention model using Busemann energy in Poincaré discs produces emotion predictions from text that generalize well even at low embedding dimensions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Keywords hyperbolic representations · affective computing · Möbius transformation · circumplex model · semantic ambiguity 1 Introduction Hyperbolic data representations emerged as a powerful paradigm in machine learning (ML) about a decade ago. Initial interest in hyperbolic ML was to a certain extent inspired by studies on low-distortion embedding of the trees in the hyperbolic plane [ 1] and on the negative curvature of complex networks [ 2]. The underlying idea is that hyperbolic geometry naturally captures important structural information of some ubiquitous datasets. Hierarchies, hypernyms and contextual uncertainties are better encoded in the negative curvature manifolds, thus enabling more compact and efficient ML models. An example of the data where hierarchical relations play an essential role are natural languages."},{"citing_arxiv_id":"2603.08965","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Semantic Level of Detail for Knowledge Graphs: Discovering Abstraction Boundaries via Spectral Heat Diffusion","primary_cat":"cs.LG","submitted_at":"2026-03-09T21:54:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}