arXiv research

This thesis uses Kantorovich-Rubinstein distance for classifying points based on their measures.

Study sharp convergence rates of empirical UOT for spatio-temporal point processes.

New algorithm solves unbalanced optimal transport on trees in quasi-linear time.

Revisits shallow neural networks using Lipschitz norms and measures.

New framework enhances neural network robustness against adversarial attacks.

On a Riemannian manifold, lower Ricci curvature bounds are known to be characterized by geodesic convexity properties of various entropies with respect to the Kantorovich-Rubinstein-Wasserstein square distance from optimal transportation. These notions also make sense in a (nonsmooth) metric measure setting, where they…

New neural method calculates EMD for particle physics data.

Paper relaxes the Lipschitz constraint in WGANs to improve performance.

This paper presents a novel method to compute the exact Kantorovich-Wasserstein distance between a pair of $d$-dimensional histograms having $n$ bins each. We prove that this problem is equivalent to an uncapacitated minimum cost flow problem on a $(d+1)$-partite graph with $(d+1)n$ nodes and $dn^{\frac{d+1}{d}}$ arcs,…

We refine and generalize several interpolation inequalities bounding the $L^p$ norm of a probability density with respect to the reference measure $μ$ by its Sobolev norm and the Kantorovich distance to $μ$ on a smooth weighted Riemannian manifold satisfying $CD(0, \infty)$ condition.

Efficiently simulates and calibrates the rough Bergomi model using Wasserstein distance.

New method calculates cut locus on Riemannian manifolds using optimal transport.

Study optimal transport on globally hyperbolic spacetimes, focusing on weak Kantorovich potentials' regularity.

Study robust distribution estimation with Wasserstein distance, achieving optimal risk.

We describe an example of a closed orientable 3-manifold with distinct distance three genus two Heegaard splittings. This demonstrates that the constructions of alternate genus two Heegaard splittings of closed orientable 3-manifolds described by Rubinstein and Scharlemann in their 1998 paper Genus Two Heegaard Splitti…

Let $P, Q$ be Heegaard surfaces of a closed orientable 3-manifold. In this paper, we introduce a method for giving an upper bound of Hempel distance of $P$ by using the Reeb graph derived from a certain horizontal arc in the ambient space $[0,1]\times[0,1]$ of the Rubinstein-Scharlemann graphic derived from $P$ and $Q$…

In this work, we present a method to compute the Kantorovich-Wasserstein distance of order one between a pair of two-dimensional histograms. Recent works in Computer Vision and Machine Learning have shown the benefits of measuring Wasserstein distances of order one between histograms with $n$ bins, by solving a classic…

This is a review of explicit computations of Connes distance in noncommutative geometry, covering finite dimensional spectral triples, almost-commutative geometries, and spectral triples on the algebra of compact operators. Several applications to physics are covered, like the metric interpretation of the Higgs field, …

Monge-Kantorovich distances, otherwise known as Wasserstein distances, have received a growing attention in statistics and machine learning as a powerful discrepancy measure for probability distributions. In this paper, we focus on forecasting a Gaussian process indexed by probability distributions. For this, we provid…

New stability theory for Sinkhorn semigroups with explicit decay rates.

Paper tackles distribution matching by partially matching distributions, achieving robust results.

Solves Cheltsov-Rubinstein problem for complex surfaces with two boundary components.

Formula derived for curvature in measure spaces.

A general duality proof for Wasserstein distributionally robust optimization.

A gap in a paper of Rubinstein-Scharlemann is explored: new examples are found of closed orientable 3-manifolds with possibly multiple genus 2 Heegaard splittings. Properties common to all the examples in the original paper are not universally shared by the new examples: some of the new examples have Hempel distance 3,…

This paper studies neural network operators and their convergence properties.

Adapts Stein's method for geometric inequalities, addressing boundary terms.

Optimal transport adapted for contaminated probabilities, showing equivalence under specific conditions.

New Fourier metrics equivalent to Wasserstein distances in image processing.

We show if M is a closed, connected, orientable, hyperbolic 3-manifold with Heegaard genus g then g >= 1/2 cosh(r) where r denotes the radius of any isometrically embedded ball in M. Assuming an unpublished result of Pitts and Rubinstein improves this to g >= 1/2 cosh(r) + 1/2. We also give an upper bound on the volume…

Muon dynamics study uses spectral Wasserstein flow for optimization stability.

This work robustifies Wasserstein distance estimation with MoM estimators for outlier-polluted data.

We study the non-asymptotic behavior of a Coulomb gas on a compact Riemannian manifold. This gas is a symmetric n-particle Gibbs measure associated to the two-body interaction energy given by the Green function. We encode such a particle system by using an empirical measure. Our main result is a concentration inequalit…

Proposes a new distance metric for multi-marginal optimal transport.

The paper is accompanying "A general Duality Theorem for the Monge-Kantorovich Transport Problem". We explain the methods used in this article in an elementary setting and present two examples complementing the results obtained therein.

Paper answers Jin and Rubinstein's question about Fano manifolds.

New methods improve stability of Sinkhorn algorithm in machine learning.

Unified approach solves Kyle model with dynamic information.

Based on a new coupling approach, we prove that the transition step of the Hamiltonian Monte Carlo algorithm is contractive w.r.t. a carefully designed Kantorovich (L1 Wasserstein) distance. The lower bound for the contraction rate is explicit. Global convexity of the potential is not required, and thus multimodal targ…

Proves uniqueness of barycenters on manifolds without restrictions.

Iterates towards Kähler metrics with constant scalar curvature.

We prove that, if $Ω\subset \mathbb{R}^n$ is an open bounded starshaped domain of class $C^2$, the constancy over $\partial Ω$ of the function $\varphi(y) = \int_0^{λ(y)} \prod_{j=1}^{n-1}[1-t κ_j(y)]\, dt$ implies that $Ω$ is a ball. Here $k_j(y)$ and $λ(y)$ denote respectively the principal curvatures and the cut v…

Optimizes angular velocity transfers for rigid bodies under deadline constraints.

In [Topology 35 (1996) 1005--1023] J H Rubinstein and M Scharlemann, using Cerf Theory, developed tools for comparing Heegaard splittings of irreducible, non-Haken manifolds. As a corollary of their work they obtained a new proof of Waldhausen's uniqueness of Heegaard splittings of S^3. In this note we use Cerf Theory …

We develop an equivariant min-max theory as proposed by Pitts-Rubinstein in 1988 and then show that it can produce many of the known minimal surfaces in $\mathbb{S}^3$ up to genus and symmetry group. We also produce several new infinite families of minimal surfaces in $\mathbb{S}^3$ proposed by Pitts-Rubinstein. These …

The notion of asymptotically log Fano varieties was given by Cheltsov and Rubinstein. We show that, if an asymptotically log Fano variety $(X, D)$ satisfies that $D$ is irreducible and $-K_X-D$ is big, then $X$ does not admit Kähler-Einstein edge metrics with angle $2πβ$ along $D$ for any sufficiently small positive ra…

Proves minimal Heegaard surfaces properties in 3-manifolds.

A major breakthrough in the theory of topological algorithms occurred in 1992 when Hyam Rubinstein introduced the idea of an almost normal surface. We explain how almost normal surfaces emerged naturally from the study of geodesics and minimal surfaces. Patterns of stable and unstable geodesics can be used to character…