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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.

168,695 papers · 148 categories

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60121181241 · Jun 202019922001200920172026
48 results for Lipschitz differentiability

New method for differentially private optimization with general Lipschitz conditions.

problem Differentially private optimization under general Lipschitz conditions.
method Generalized Lipschitz condition for per-sample gradients, tuning clip norm based on minimum per-sample Lipschitz constant.
result Efficacy of the recommended clip norm tuning method verified on 8 datasets.

Study proves existence and uniqueness for differential equations with non-Lipschitz coefficients.

problem Existence and uniqueness for differential equations with non-Lipschitz coefficients.
method Relying on robust Itô integration, prove existence and uniqueness results.
result Existence and uniqueness for one-dimensional differential equations with non-Lipschitz coefficients.

The paper studies Lipschitz bounds for integral kernels under differentiability assumptions.

problem Understanding the Lipschitz continuity of feature maps associated with integral kernels.
method Analyzes differentiability assumptions to derive explicit formulas for Lipschitz constants and conditions for non-Lipschitz continuity.
result Explicit formulas and conditions for Lipschitz continuity of feature maps associated with various kernels.

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 ↗

A generalization of the Flow-box Theorem is given. The assumption of continuous differentiability of the vector field is relaxed to a local Lipschitz condition. The theorem holds in any Banach space.

2003-05-14abs ↗pdf ↗

Paper proves convergence for private FL on non-Lipschitz convex objectives using normalization instead of clipping.

problem Lack of convergence results for differentially private federated learning with non-Lipschitz objectives.
method Developed a convergence result for private FL on smooth convex objectives without assuming Lipschitzness, using normalization instead of clipping.
result Normalization-based private FL algorithm converges better than clipping-based counterpart on smooth convex functions.

Privacy affects fairness in classification models, but not drastically.

problem The impact of differential privacy on fairness in classification models.
method Theoretical analysis proving Lipschitz continuity of fairness measures and a non-asymptotic bound on fairness levels.
result Privacy impacts fairness, but not significantly as the number of samples increases.

We study continuous maps between differential manifolds from a microlocal point of view. In particular, we characterize the Lipschitz continuity of these maps in terms of the microsupport of the constant sheaf on their graph. Furthermore, we give lower and upper bounds on the microsupport of the graph of a continuous m…

2016-11-14abs ↗pdf ↗

We give a new approach to the infinitesimal structure of Lipschitz maps into L^1. As a first application, we give an alternative proof of the main theorem from an earlier paper, that the Heisenberg group does not admit a bi-Lipschitz embedding in L^1. The proof uses the metric differentiation theorem of Pauls and the c…

2009-07-19abs ↗pdf ↗

Optimizes privacy-preserving optimization for heavy-tailed data.

problem Privacy-preserving optimization with heavy-tailed gradients.
method Pure ε-differential privacy framework for Lipschitz extensions.
result Minimax optimal excess-risk rate for pure ε-DP heavy-tailed SCO.

We consider the problem of finding sufficient conditions for a locally Lipschitz mapping between Finsler manifolds to be a global homeomorphism. For this purpose, we develop the notion of Clarke generalized differential in this context and, using this, we obtain a version of the Hadamard integral condition for invertib…

2012-01-23abs ↗pdf ↗

Basic aspects of differential geometry can be extended to various non-classical settings: Lipschitz manifolds, rectifiable sets, sub-Riemannian manifolds, Banach manifolds, Weiner space, etc. Although the constructions differ, in each of these cases one can define a module of measurable 1-forms and a first-order exteri…

1998-07-17abs ↗pdf ↗

We give a definition of convergence of differential of Lipschitz functions with respect to measured Gromov-Hausdorff topology. As their applications, we give a characterization of harmonic functions with polynomial growth on asymptotic cones of manifolds with nonnegative Ricci curvature and Euclidean volume growth, and…

2010-05-06abs ↗pdf ↗

Improved DP SO with large Lipschitz parameters, handling outliers and heavy-tailed data.

problem Differential privacy in stochastic optimization with large Lipschitz parameters.
method Assumes bounded k-th order moments, provides linear-time algorithms for smooth convex and non-smooth convex losses.
result Improved risk bounds scaling with k-th moment, not uniform Lipschitz parameter.

Defines a new modulus for Lipschitz surfaces and proves a homological duality theorem.

problem Lipschitz homology classes and their moduli.
method Defining a new modulus dModp\operatorname{dMod}_p and proving a homological duality theorem.
result Every relative Lipschitz kk-homology class has a unique dual class satisfying a specific modulus product equality.

Sharp Lipschitz bounds and gradient estimates for fully nonlinear parabolic equations.

problem Understanding moduli of continuity for fully nonlinear parabolic equations.
method Proving moduli of continuity of viscosity solutions are subsolutions of one-dimensional parabolic equations.
result Sharp Lipschitz bounds and gradient estimates for fully nonlinear parabolic equations with bounded initial data.

New approach achieves optimal rates for differentially private stochastic convex optimization with heavy-tailed gradients.

problem Differentially private stochastic convex optimization with heavy-tailed gradients.
method Reduction-based approach to achieve optimal rates.
result Achieved optimal rates up to logarithmic factors, nearly matching a lower bound.

Paper proves convergence of Markovian iteration for FBSDEs with fully coupled drift and Z process.

problem Proving convergence of Markovian iteration for FBSDEs with fully coupled drift and Z process.
method Differentiation-based approach to handle Z process, uniformly controlling Lipschitz continuity of decoupling fields.
result Proves convergence of Markovian iteration method for FBSDEs with fully coupled drift and Z process.

Corrected Monti's blow-up analysis for H-minimizing sets in Heisenberg group.

problem Blow-up analysis of H-minimizing sets in Heisenberg group with corrected partial differential equation.
method Revised Monti's results on blow-ups of H-perimeter minimizing sets in Hn\mathbb{H}^n and corrected the partial differential equation for the limit function.
result Corrected the partial differential equation for the limit function of blow-ups in Heisenberg group.

We model how Lipschitz continuity changes during neural network training.

problem Understanding how Lipschitz continuity evolves during training.
method We use a system of stochastic differential equations to capture the dynamics of Lipschitz continuity under SGD.
result We identify three factors driving the evolution of Lipschitz continuity: gradient flow projection, gradient noise, and Hessian projection.

Generalization bounds derived for neural ODEs and deep residual networks.

problem Understanding the generalization capability of neural ODEs and deep residual networks.
method Lipschitz-based argument and analogy with deep residual networks.
result A generalization bound involving the magnitude of weight matrix differences.

Smooths metrics on manifolds with curvature bounds and injectivity radius constraints.

problem Smooth metrics on manifolds with curvature and injectivity constraints.
method Bi-Lipschitz smoothing with controlled smoothing and volume lower bounds.
result Proves existence of smooth metrics with curvature bounds and injectivity radius constraints.

New method for efficient proximal mapping of 1-path-norm in shallow networks.

problem Efficiently handling the 1-path-norm of shallow neural networks.
method Closed-form proximal operator for efficient computation and upper bound on Lipschitz constant.
result Proximal mapping allows robust training against adversarial perturbations.

We establish that over a C^{2,1} manifold the exponential map of any Lipschitz connection or spray determines a local Lipeomophism and that, furthermore, reversible convex normal neighborhoods do exist. To that end we use the method of Picard-Lindelof approximation to prove the strong differentiability of the exponenti…

2013-08-30abs ↗pdf ↗

Let ΣΣ be a hypersurface in an nn-dimensional Riemannian manifold MM, n2n\geqslant 2. We study the isometric extension problem for isometric immersions f:ΣRnf:Σ\to\mathbb R^n, where Rn\mathbb R^n is equipped with the Euclidean standard metric. We prove a general curvature obstruction to the existence of merely differen…

2015-01-13abs ↗pdf ↗

The paper sharpens a theorem about surfaces with zero Gaussian curvature.

problem Quantifying the isometric property of surfaces with zero Gaussian curvature.
method Asymptotically sharp quantitative version of a classical theorem using isothermal coordinates.
result An isothermal coordinate map from a Riemannian disc to an Euclidean disc is bi-Lipschitz with a constant of exp(4ε).

Paper tackles robust control of SDEs with ambiguity, proving value function existence and applying to investment problems.

problem Robust control of SDEs with ambiguity parameters and non-Lipschitz coefficients.
method Existence and uniqueness of value function established through BSDEs with non-linear growth conditions.
result Existence and uniqueness of value function in proper space, verified through BSDEs.

New privacy bounds for DP-SGD's last iterate, even with cyclic sampling.

problem Privacy of the last iterate in DP-SGD with cyclic sampling.
method Established new RDP upper bounds for the last iterate under realistic assumptions.
result Privacy bounds for DP-SGD's last iterate with cyclic sampling and clipping, even for nonconvex losses.

Proves continuum limits of Lipschitz learning using Γ-convergence.

problem Semi-supervised learning with graph-based methods and continuum limits of pp-Laplacian learning.
method Proves continuum limits of Lipschitz learning using Γ-convergence.
result Proves ΓΓ-convergence in the LL^\infty-topology to the supremum norm of the gradient.

Let ρ:GO(V)ρ: G \rightarrow \operatorname{O}(V) be a real finite dimensional orthogonal representation of a compact Lie group, let σ=(σ1,,σn):VRnσ= (σ_1,\ldots,σ_n) : V \to \mathbb R^n, where σ1,,σnσ_1,\ldots,σ_n form a minimal system of homogeneous generators of the GG-invariant polynomials on VV, and set $d = \max_i \operatorname{deg} …

2014-06-10abs ↗pdf ↗

We show for k2k \geq 2 that the locally Lipschitz viscosity solution to the σkσ_k-Loewner-Nirenberg problem on a given annulus {a<x<b}\{a < |x| < b\} is Cloc1,1kC^{1,\frac{1}{k}}_{\rm loc} in each of {a<xab}\{a < |x| \leq \sqrt{ab}\} and {abx<b}\{\sqrt{ab} \leq |x| < b\} and has a jump in radial derivative across x=ab|x| = \sqrt{ab}. Further…

2020-01-13abs ↗pdf ↗

We prove that, given an RCD(K,N)RCD^{*}(K,N)-space (X,d,m)(X,d,m), then it is possible to mm-essentially cover XX by measurable subsets (Ri)iN(R_{i})_{i\in \mathbb{N}} with the following property: for each ii there exists kiN[1,N]k_{i} \in \mathbb{N}\cap [1,N] such that mRim\llcorner R_{i} is absolutely continuous with respect to the $k_…

2016-07-07abs ↗pdf ↗

In the 1970s and again in the 1990s, Gromov gave a number of theorems and conjectures motivated by the notion that the real homotopy theory of compact manifolds and simplicial complexes influences the geometry of maps between them. The main technical result of this paper supports this intuition: we show that maps of di…

2017-12-31abs ↗pdf ↗

Paper proves DN map determination for simple surfaces with low regularity metrics.

problem Determining DN map from scattering relation for surfaces with low regularity metrics.
method Modified technical results and used microlocal analysis for metrics with finite regularity.
result Scattering relation determines DN map for C17C^{17} surfaces, and for C1,1C^{1,1} metrics using Lipschitz distance function.

Flow Matching improves Wasserstein 1 distance convergence in high dimensions.

problem Improving Wasserstein 1 distance estimation for unbounded distributions.
method Flow Matching approach based on ODEs, controlling Lipschitz constant.
result Derives a convergence rate for Wasserstein 1 distance, improving previous results.