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Given a smooth 2-dimensional Riemannian or pseudo-Riemannian manifold $(M, \boldsymbol{g})$ and an ambient 3-dimensional Riemannian or pseudo-Riemannian manifold $(N, \boldsymbol{h})$, one can ask under what circumstances does the exterior…

Differential Geometry · Mathematics 2018-01-03 Jeanne Clelland , Thomas Ivey , Naghmana Tehseen , Peter Vassiliou

For any finite point set in $D$-dimensional space equipped with the 1-norm, we present random linear embeddings to $k$-dimensional space, with a new metric, having the following properties. For any pair of points from the point set that are…

Probability · Mathematics 2020-11-09 Michael P. Casey

The metric sketching problem is defined as follows. Given a metric on $n$ points, and $\epsilon>0$, we wish to produce a small size data structure (sketch) that, given any pair of point indices, recovers the distance between the points up…

Computational Geometry · Computer Science 2016-11-30 Piotr Indyk , Tal Wagner

Let $(X,\left\Vert \cdot \right\Vert )$ be a real normed space of dimension $N\in \mathbb{N}$ with a basis $(e_{i})_{1}^{N}$ such that the norm is invariant under coordinate permutations. Assume for simplicity that the basis constant is at…

Functional Analysis · Mathematics 2014-01-03 Daniel Fresen

An important problem in machine learning theory is to understand the approximation and generalization properties of two-layer neural networks in high dimensions. To this end, researchers have introduced the Barron space…

Machine Learning · Statistics 2024-01-02 Lei Wu

A classic problem in unsupervised learning and data analysis is to find simpler and easy-to-visualize representations of the data that preserve its essential properties. A widely-used method to preserve the underlying hierarchical structure…

Data Structures and Algorithms · Computer Science 2020-08-18 Vincent Cohen-Addad , Karthik C. S. , Guillaume Lagarde

Model merging aims to combine multiple models into one without additional training. Na\"ive parameter-space averaging can be fragile under architectural symmetries, as their geometry does not take them into account. In this work we show…

Machine Learning · Computer Science 2026-05-08 Marvin F. da Silva , Mohammed Adnan , Felix Dangel , Sageev Oore

It is shown that for every $K>0$ and $\e\in (0,1/2)$ there exist $N=N(K)\in \N$ and $D=D(K,\e)\in (1,\infty)$ with the following properties. For every separable metric space $(X,d)$ with doubling constant at most $K$, the metric space…

Metric Geometry · Mathematics 2010-12-13 Assaf Naor , Ofer Neiman

We first prove that for every metrizable space $X$, for every closed subset $F$ whose complement is zero-dimensional, the space $X$ can be embedded into a product space of the closed subset $F$ and a metrizable zero-dimensional space as a…

General Topology · Mathematics 2026-01-13 Yoshito Ishiki

Coarse geometry studies metric spaces on the large scale. The recently introduced notion of coarse entropy is a tool to study dynamics from the coarse point of view. We prove that all isometries of a given metric space have the same coarse…

Metric Geometry · Mathematics 2025-03-04 William Geller , Michał Misiurewicz , Damian Sawicki

Two central limit theorems for sample Fr\'echet means are derived, both significant for nonparametric inference on non-Euclidean spaces. The first one, Theorem 2.2, encompasses and improves upon most earlier CLTs on Fr\'echet means and…

Statistics Theory · Mathematics 2016-03-29 Rabi Bhattacharya , Lizhen Lin

We study representations of data from an arbitrary metric space $\mathcal{X}$ in the space of univariate Gaussian mixtures with a transport metric (Delon and Desolneux 2020). We derive embedding guarantees for feature maps implemented by…

Machine Learning · Computer Science 2023-10-17 Anastasis Kratsios , Valentin Debarnot , Ivan Dokmanić

A Boolean algebra $\A$ equipped with a (finitely-additive) positive probability measure $m$ can be turned into a metric space $(\A , d_{m})$, where $d_{m}(a,b)= m ((a\wedge\neg b)\vee(\neg a\wedge b))$, for any $a,b\in A$, sometimes…

Logic · Mathematics 2022-08-01 Stefano Bonzio , Andrea Loi

An isometric embedding of a graph into a metric space is an embedding of the vertices such that the smallest number of edges connecting any two vertices equals to the distance in the metric space between the images. In this paper, we study…

Metric Geometry · Mathematics 2018-04-20 Shiquan Ren

While there exists a well-developed asymptotic theory of Fr\'echet means of random variables taking values in a general "finite-dimensional" metric space, there are only a few known results in which the random variables can take values in…

Probability · Mathematics 2024-12-30 Adam Quinn Jaffe

It is known that if finite subsets of a locally finite metric space $M$ admit $C$-bilipschitz embeddings into $\ell_p$ $(1\le p\le \infty)$, then for every $\epsilon>0$, the space $M$ admits a $(C+\epsilon)$-bilipschitz embedding into…

Functional Analysis · Mathematics 2019-10-10 Sofiya Ostrovska , Mikhail I. Ostrovskii

We study the old problem of isometrically embedding a 2-dimensional Riemannian manifold into Euclidean 3-space. It is shown that if the Gaussian curvature vanishes to finite order and its zero set consists of two Lipschitz curves…

Analysis of PDEs · Mathematics 2014-01-17 Qing Han , Marcus Khuri

Consider the sum of the first $N$ eigenspaces for the Laplacian on a Riemannian manifold. A basis for this space determines a map to Euclidean space and for $N$ sufficiently large the map is an embedding. In analogy with a fruitful idea of…

Differential Geometry · Mathematics 2014-04-30 Eric Potash

Goemans showed that any $n$ points $x_1, \dotsc x_n$ in $d$-dimensions satisfying $\ell_2^2$ triangle inequalities can be embedded into $\ell_{1}$, with worst-case distortion at most $\sqrt{d}$. We extend this to the case when the points…

Data Structures and Algorithms · Computer Science 2015-12-15 Amit Deshpande , Prahladh Harsha , Rakesh Venkat

We prove a rigidity theorem that shows that, under many circumstances, quasi-isometric embeddings of equal rank, higher rank symmetric spaces are close to isometric embeddings. We also produce some surprising examples of quasi-isometric…

Differential Geometry · Mathematics 2018-06-13 David Fisher , Kevin Whyte