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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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63126189252 · Jun 202019922001200920182026
48 results for Lipschitz connectivity

Gromov's question answered with quadratic dependence on Lipschitz constant.

problem Dependence of optimal nullhomotopy Lipschitz constant on Lipschitz constant, m, and n.
method Constructing nullhomotopies with thickness and width bounds.
result Thickness is C(m,n)(L+1)C(m,n)(L+1) and width is C(m,n)(L+1)2C(m,n)(L+1)^2.

LiPopt uses polynomial optimization to estimate neural network Lipschitz constants efficiently.

problem Estimating the Lipschitz constant of neural networks efficiently.
method Sparse polynomial optimization, leveraging network connectivity to reduce complexity.
result Superior estimates of the \ell_\infty-Lipschitz constant compared to existing methods.

Conic singular sub-manifolds are Lipschitz Normally Embedded in compact non-Euclidean manifolds.

problem Understanding the Lipschitz geometry of conic singular sub-manifolds.
method Analyzing the metric properties of conic singular sub-manifolds in compact non-Euclidean manifolds.
result Connected conic singular sub-manifolds are Lipschitz Normally Embedded.

Lipschitz mappings found between Riemann surfaces with specific properties.

problem Finding globally Lipschitz mappings between doubly connected Riemann surfaces.
method Using a result from Iwaniec, Kovalev, and Onninen, the minimizer of the energy functional is shown to be locally Lipschitz and globally Lipschitz.
result The minimizer of the energy functional is a globally Lipschitz mapping.

We construct a smooth compact n-dimensional manifold Y with one point singularity such that all its Lipschitz homotopy groups are trivial, but Lipschitz mappings Lip(S^n,Y) are not dense in the Sobolev space W^{1,n}(S^n,Y). On the other hand we show that if a metric space Y is Lipschitz (n-1)-connected, then Lipschitz …

2013-06-27abs ↗pdf ↗

New method prevents gradient attenuation in Lipschitz constrained convolutional networks.

problem Gradient norm attenuation in Lipschitz constrained convolutional networks.
method Block Convolution Orthogonal Parameterization (BCOP) to train scalable, expressive, provably Lipschitz convolutional networks.
result Empirically, BCOP parameterization is competitive with existing approaches to provable adversarial robustness and Wasserstein distance estimation.

We characterize locally Lipschitz mappings and existence of Lipschitz extensions through a first order nonlinear system of PDEs. We extend this study to graded group-valued Lipschitz mappings defined on compact Riemannian manifolds. Through a simple application, we emphasize the connection between these PDEs and the Ru…

2007-11-30abs ↗pdf ↗

The study proves surfaces in a specific Heisenberg group must be simple planes.

problem Characterizing surfaces in a sub-Finsler Heisenberg group.
method Analyzes (X,Y)(X,Y)-Lipschitz surfaces in H1\mathbb{H}^1 with a sub-Finsler structure.
result Complete, oriented, stable (X,Y)(X,Y)-Lipschitz surfaces are vertical planes.

Smooth approximations of Lipschitz maps via Ehresmann fibrations and Reeb sphere theorem for functions.

problem Approximating Lipschitz maps and understanding singular points in Riemannian manifolds.
method Using Ehresmann fibrations and Reeb's sphere theorem for Lipschitz functions.
result A Lipschitz map can be approximated by a smooth map via Ehresmann fibrations.

A map between manifolds is an isometry if it's Lipschitz and scalar curvature bounded.

problem Characterizing maps between manifolds based on their scalar curvature and Lipschitz continuity.
method Spectral properties of Dirac operators and index theory for low regularity metrics and bundles.
result A 1-Lipschitz map between manifolds is an isometry if it has bounded scalar curvature.

The study connects polyhedral manifolds to Riemannian ones with geometric bounds.

problem Connecting polyhedral manifolds to Riemannian manifolds with geometric constraints.
method Using a theorem by C. Lange and B. Bowditch, the study bounds the curvature and injectivity radius of Riemannian manifolds.
result Polyhedral manifolds with bounded geometry are bi-Lipschitz homeomorphic to Riemannian manifolds with controlled curvature and injectivity radius.

Paper investigates Lipschitz constants of self-attention modules in neural networks.

problem Lipschitz constants of self-attention modules in neural networks.
method Proved standard dot-product self-attention is not Lipschitz for unbounded input domain. Proposed L2 self-attention that is Lipschitz. Derived upper bound on L2 self-attention's Lipschitz constant.
result Proved standard self-attention is not Lipschitz for unbounded input domain and proposed an alternative L2 self-attention that is Lipschitz.

Study links between surface germs and knot theory in 4D.

problem Understanding the relationship between surface germs and knot theory in R4\mathbb{R}^4.
method Constructing surface germs XKX_K linked to knots KK in S3S^3 and studying their Lipschitz geometry.
result Ambient bi-Lipschitz equivalence of surface germs is related to isotopy of knots, and Jones polynomial can recognize non-equivalent germs.

The paper calculates bounds on the local Lipschitz constants of neural network layers.

problem Understanding the Lipschitz constants of neural network layers for robustness analysis.
method Analytical approach to determine upper bounds on local Lipschitz constants of affine-ReLU functions.
result The method produces tighter bounds than the standard conservative bound, especially for small perturbations.

The abstract discusses the classification of 3D Lie groups with Riemannian metrics.

problem Classifying 3D Lie groups up to quasi-isometries and bi-Lipschitz equivalence.
method Review of existing literature and study of quasi-isometry and bi-Lipschitz equivalence.
result For three-dimensional simply connected groups, quasi-isometry implies isometry with suitable metrics.

This paper refines homotopy theory for cubical sets and uniform spaces.

problem Classical homotopy theory limitations in cubical sets and uniform spaces.
method Develops a uniform-theoretic refinement for cubical sets and uniform spaces, lifting to a full and faithful embedding.
result Lifts classical homotopy categories to new uniform homotopy categories, generalizing cohomology theories.

New parameterization of neural networks with Lipschitz bounds for robustness.

problem Developing robust neural networks with Lipschitz bounds.
method Introducing a new parameterization that admits a Lipschitz bound during training without requiring projections or barrier functions.
result The new parameterization improves robustness to adversarial attacks in image classification.

Study on metric spaces with Möbius self-homeomorphisms and their properties.

problem Characterizing metric spaces with specific homogeneity properties.
method Investigation of homogeneity with Möbius and quasi-Möbius self-homeomorphisms.
result New characterization of snowflakes of boundaries of rank-one symmetric spaces.

Study Lipschitz regularity for manifold-constrained ROF model on curved surfaces.

problem Lipschitz regularity for manifold-constrained ROF model on curved surfaces.
method Generalization of ROF model, existence and uniqueness of minimizers, regularity results on PDE system.
result Lipschitz regularity of minimizers without convexity requirements.

Generalised regularisation equals robustness for exotic function classes.

problem Adversarial examples highlight the need for robust models in exotic function classes.
method Equality result linking distributional robustness and Lipschitz regularisation.
result Certified robustness of Lipschitz-regularised models with mild assumptions.

The study connects lamination and orbit closures in hyperbolic manifolds.

problem Understanding the geometric and dynamical properties of horocycle orbit closures in Z\mathbb{Z}-covers of compact hyperbolic manifolds.
method Exposes connections between distance minimizing laminations and horospherical orbit closures in Z\mathbb{Z}-covers of compact hyperbolic manifolds. Provides novel constructions and explicit descriptions.
result Even slight perturbations to hyperbolic metrics can drastically change horocycle orbit closures.

The study examines metrics on Riemannian spaces with bounded properties and finds conditions for Lipschitz and uniform bounds.

problem Investigating bounded rough Riemannian metrics and their properties.
method Analyzing the structure of bounded rough Riemannian metrics and finding conditions for Lipschitz and uniform bounds.
result Weak conditions are identified for Lipschitz and uniform bounds on the metrics.

The paper examines convergence of distances in Lipschitz structures on manifolds.

problem Convergence of distances in Lipschitz vector fields and norms on manifolds.
method Analysis of convergence of distances associated to converging structures of Lipschitz vector fields and norms.
result Under mild controllability assumption, distances converge locally uniformly to the limit Carnot-Carathéodory distance.

The study links model generalization to the Hessian and Lipschitz constant.

problem Understanding and improving model generalization in neural networks.
method Connecting model generalization to the Hessian and Lipschitz constant, proposing a metric and optimization algorithm.
result Model generalization is related to the Hessian and Lipschitz constant, providing a new metric and optimization algorithm.

New method approximates quadratic-growth BSDEs with short-term expansions.

problem Approximating solutions to quadratic-growth Backward Stochastic Differential Equations (BSDEs).
method Connecting semi-analytic asymptotic expansions over short-time intervals.
result Avoids Monte Carlo simulation and numerical integrations for estimating conditional expectations.

New Lipschitz bound for ReLU networks resists weight rescaling.

problem Lack of robustness guarantees for ReLU networks under weight perturbations.
method Rescaling-invariant Lipschitz bound based on path-metrics.
result The new bound applies to various ReLU-DAG architectures and resists neuron-wise rescalings.

Adversarial robust models have more interpretable saliency maps.

problem Understanding the relationship between adversarial robustness and saliency map interpretability.
method Quantifying the alignment between input images and saliency maps, hypothesizing and testing the relationship with linear and neural network models.
result The alignment between input images and saliency maps increases as the distance to the decision boundary grows, especially in linear models.

This is one of a series of papers examining the interplay between differentiation theory for Lipschitz maps, X-->V, and bi-Lipschitz nonembeddability, where X is a metric measure space and V is a Banach space. Here, we consider the case V=L^1 where differentiability fails. We establish another kind of differentiability…

2006-11-30abs ↗pdf ↗

Proposes a weaker version of Strong Cosmic Censorship with curvature bounds.

problem The original Strong Cosmic Censorship conjecture.
method Weakens the conjecture to allow manifolds with bounded curvature and Lipschitz continuity of metrics.
result Proves the conjecture with bounded curvature for sufficiently large p (p>4 with uniform bounds, p>2 without uniform bounds).

New findings on metric spaces with finite Nagata dimension.

problem Understanding isoperimetric properties in subsets of metric spaces.
method Analyzing quasiconvex subsets with finite Nagata dimension and applying isoperimetric inequalities.
result Quasiconvex subsets of metric spaces with finite Nagata dimension are isoperimetrically undistorted up to a certain dimension.

The paper studies maximal stretch and Lipschitz maps on negatively curved manifolds.

problem Investigating maximal stretch and Lipschitz maps on negatively curved manifolds.
method Defined maximal stretch for negatively curved manifolds and connected it to best Lipschitz maps.
result The Mather set may not be lifts of geodesic laminations but shares similar features.

Graph-based framework for provably robust adversarial training.

problem Adversarial robustness of machine learning models.
method Formulates adversarial robustness as loss minimization with a Lipschitz constraint, using graph-based discretization and primal-dual algorithms.
result Establishes a connection between elliptic operators and adversarial learning, and proves fundamental lower bounds on adversarial sensitivity.

The paper proves inequalities linking geometric norms and Thurston norms in hyperbolic 3-manifolds.

problem Inequalities linking geometric norms and Thurston norms in hyperbolic 3-manifolds.
method Analyzes geometric L2L^2-norms, Thurston norms, and Lipschitz maps to prove inequalities.
result Proves an inequality between geometric L2L^2-norm and Thurston norm, qualitatively sharp.

The purpose of the paper is to characterize the dimension of sublinear Higson corona νL(X)ν_L(X) of XX in terms of Lipschitz extensions of functions: Theorem: Suppose (X,d)(X,d) is a proper metric space. The dimension of the sublinear Higson corona νL(X)ν_L(X) of XX is the smallest integer m0m\ge 0 with the following property…

2006-08-28abs ↗pdf ↗

Decentralized learning for GLMs with feature distribution and network connectivity.

problem Optimizing generalized linear models in a decentralized network with feature partitioning.
method Chambolle--Pock primal--dual algorithm applied to an equivalent saddle-point formulation.
result Convergence rates for empirical risk minimization under Lipschitz and square root Lipschitz assumptions.