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A conformal metric $g$ with constant curvature one and finite conical singularities on a compact Riemann surface $\Sigma$ can be thought of as the pullback of the standard metric on the 2-sphere by a multi-valued locally univalent…

Differential Geometry · Mathematics 2016-01-20 Qing Chen , Wei Wang , Yingyi Wu , Bin Xu

We introduce directional regularity, a new definition of anisotropy for multivariate functional data. Instead of taking the conventional view, which determines anisotropy as a notion of smoothness along a dimension, directional regularity…

Methodology · Statistics 2026-05-05 Omar Kassi , Sunny G. W. Wang

We design a deterministic algorithm that, given $n$ points in a \emph{typical} constant degree regular~graph, queries $O(n)$ distances to output a constant factor approximation to the average distance among those points, thus answering a…

Data Structures and Algorithms · Computer Science 2025-10-22 Alexandros Eskenazis , Manor Mendel , Assaf Naor

This paper proves fixed domain asymptotic results for estimating a smooth invertible transformation $f:\Bbb{R}^2\to\Bbb{R}^2$ when observing the deformed random field $Z\circ f$ on a dense grid in a bounded, simply connected domain…

Statistics Theory · Mathematics 2009-08-21 Ethan Anderes , Sourav Chatterjee

We study the question of approximating a compact geodesic metric space by metric graphs satisfying a uniform upper bound on their first Betti number. We prove that, up to a suitable multiplicative constant, Reeb graphs of distance functions…

Metric Geometry · Mathematics 2023-10-27 Facundo Memoli , Osman Berat Okutan , Qingsong Wang

A strong law of large numbers for $d$-dimensional random projections of the $n$-dimensional cube is derived. It shows that with respect to the Hausdorff distance a properly normalized random projection of $[-1,1]^n$ onto $\mathbb{R}^d$…

Probability · Mathematics 2019-10-08 Zakhar Kabluchko , Joscha Prochno , Christoph Thaele

Given a `cost' functional $F$ on paths $\gamma$ in a domain $D\subset\mathbb{R}^d$, in the form $F(\gamma) = \int_0^1 f(\gamma(t),\dot\gamma(t))dt$, it is of interest to approximate its minimum cost and geodesic paths. Let $X_1,\ldots, X_n$…

Probability · Mathematics 2017-11-21 Erik Davis , Sunder Sethuraman

We introduce the boundary measure at scale r of a compact subset of the n-dimensional Euclidean space. We show how it can be computed for point clouds and suggest these measures can be used for feature detection. The main contribution of…

Computational Geometry · Computer Science 2010-03-31 Frédéric Chazal , David Cohen-Steiner , Quentin Mérigot

Investigating a model of scale-invariant random spatial network suggested by Aldous, Kendall constructed a random metric $T$ on $\mathbb{R}^d$, for which the distance between points is given by the optimal connection time, when travelling…

Probability · Mathematics 2023-01-31 Guillaume Blanc

We extend a well-known theorem by Jones and Makarov [JM] on the singularity of boundary distortion of planar conformal mappings. We use a different technique to recover the previous result and, moreover, generalize the result for…

Complex Variables · Mathematics 2008-02-19 Tomi Nieminen , Ignacio Uriarte-Tuero

Quasisymmetry is a well-studied property of homeomorphisms between metric spaces, and Ahlfors regular conformal dimension is a quasisymmetric invariant. In the present paper, we consider the Ahlfors regular conformal dimension of metrics on…

Probability · Mathematics 2026-04-06 Kôhei Sasaya

We consider a geometrically finite discrete group of conformal transformations of the sphere. Further we consider distributions which are supported on the limit set and are invariant with conformal weight. We estimate their regularity in…

Differential Geometry · Mathematics 2007-05-23 Ulrich Bunke , Martin Olbrich

Let $G_n$ be a random geometric graph with vertex set $[n]$ based on $n$ i.i.d.\ random vectors $X_1,\ldots,X_n$ drawn from an unknown density $f$ on $\R^d$. An edge $(i,j)$ is present when $\|X_i -X_j\| \le r_n$, for a given threshold…

Machine Learning · Statistics 2023-11-23 Caelan Atamanchuk , Luc Devroye , Gabor Lugosi

We prove some sharp isoperimetric type inequalities for domains with smooth boundary on Riemannian manifolds. For example, using generalized convexity, we show that among all domains with a lower bound $l$ for the cut distance and Ricci…

Differential Geometry · Mathematics 2019-11-12 Kwok-Kun Kwong

This paper introduces a framework for uncertainty quantification in regression models defined in metric spaces. Leveraging a newly defined notion of homoscedasticity, we develop a conformal prediction algorithm that offers finite-sample…

Machine Learning · Statistics 2025-07-22 Gábor Lugosi , Marcos Matabuena

We consider the problem of prescribing the Gaussian and the geodesic curvatures of a compact surface with boundary by a conformal deformation of the metric. We derive some existence results using a variational approach, either by…

Analysis of PDEs · Mathematics 2019-01-29 Rafael López-Soriano , Andrea Malchiodi , David Ruiz

Families of conformal field theories are naturally endowed with a Riemannian geometry which is locally encoded by correlation functions of exactly marginal operators. We show that the curvature of such conformal manifolds can be computed…

High Energy Physics - Theory · Physics 2023-08-09 Bruno Balthazar , Clay Cordova

In this article we study the Ahlfors regular conformal gauge of a compact metric space $(X,d)$, and its conformal dimension $\mathrm{dim}_{AR}(X,d)$. Using a sequence of finite coverings of $(X,d)$, we construct distances in its Ahlfors…

Metric Geometry · Mathematics 2012-05-08 Matias Carrasco Piaggio

The Hausdorff distance is a measure of (dis-)similarity between two sets which is widely used in various applications. Most of the applied literature is devoted to the computation for sets consisting of a finite number of points. This has…

Metric Geometry · Mathematics 2020-09-22 Daniel Kraft

We address the problem of distribution shift in unsupervised domain adaptation with a moment-matching approach. Existing methods typically align low-order statistical moments of the source and target distributions in an embedding space…

Machine Learning · Computer Science 2025-10-17 Shayan Gharib , Marcelo Hartmann , Arto Klami