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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.

168,742 papers · 148 categories

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113225338450 · May 202619922001200920172026
48 results for sharp Lipschitz bounds

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.

Sharp Lipschitz bounds for flow-matching and diffusion models with optimal sampling rates.

problem Establishing optimal Lipschitz regularity for flow-matching and diffusion models.
method Sharp Lipschitz regularity theory for flow-matching vector fields and diffusion-model scores.
result Achieves optimal sampling rate of d/N\sqrt{d}/N for Euler-type samplers in dimension dd.

The study establishes minimax bounds for estimating operators from noisy samples.

problem Estimating unknown operators between Hilbert spaces from noisy data.
method Developed a minimax theory for uniformly bounded Lipschitz operators, proving lower and upper bounds.
result Sharp characterizations of minimax risk for generic Lipschitz operators, showing a curse of sample complexity.

Sharp 2-Wasserstein bounds for DDPMs derived from Föllmer process.

problem Sampling error bounds for DDPMs in 2-Wasserstein distance.
method Lipschitz-type conditions on score function, Föllmer process, and log-concave target distributions.
result Sharp upper bounds for DDPMs in 2-Wasserstein distance, optimal in dimension and steps.

Optimization in the presence of sharp (non-Lipschitz), unpredictable (w.r.t. time and amount) changes is a challenging and largely unexplored problem of great significance. We consider the class of piecewise Lipschitz functions, which is the most general online setting considered in the literature for the problem, and …

2019-07-22abs ↗pdf ↗

Efficiently learns Single-Index Models with constant factor approximation.

problem Learning Single-Index Models under L22L_2^2 loss with unknown link functions.
method An efficient algorithm using alignment sharpness for optimization.
result Achieves constant factor approximation to optimal loss for various distributions and link functions.

We provide sharp empirical estimates of expectation, variance and normal approximation for a class of statistics whose variation in any argument does not change too much when another argument is modified. Examples of such weak interactions are furnished by U- and V-statistics, Lipschitz L-statistics and various error f…

2018-03-11abs ↗pdf ↗

New bounds for neural networks ensure robustness and accuracy.

problem Ensuring robustness of neural networks by computing Lipschitz constants.
method Analyzed and proposed new bounds for l1l^1 and ll^\infty norms, using explicit and implicit methods for convnets.
result One of the new bounds is optimal and more accurate than existing ones.

Full-batch GD achieves generalization close to any stationary point with fewer assumptions.

problem Generalization and excess risk bounds for smooth losses, including non-Lipschitz and nonconvex cases.
method Path-dependent analysis of GD's generalization error, focusing on optimization error and stability.
result Generalization error is tightly bound in terms of optimization error and iteration count, bypassing common assumptions.

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.

Nonparametric Thompson Sampling achieves optimal regret for risk-averse bandits with sub-Gaussian rewards.

problem Optimizing risk-averse bandit problems with sub-Gaussian rewards.
method Anchor-free nonparametric Thompson Sampling algorithm ρextNPTSSGρ ext{-}NPTS_{\mathrm{SG}}.
result Achieves regret matching the instance-dependent lower bound to leading order in logn\log n.

The paper proves topological stability between RCD spaces and Riemannian manifolds.

problem Proving topological stability between RCD spaces and Riemannian manifolds.
method Using Gromov-Hausdorff distance and regular homeomorphisms, the paper constructs a map between spaces.
result There exists a regular homeomorphism between RCD spaces and Riemannian manifolds under certain conditions.

Extends involutivity to non-Lipschitz subbundles and proves the Frobenius Theorem.

problem Defining involutivity for non-Lipschitz subbundles and proving the Frobenius Theorem.
method Using generalized functions, the Frobenius Theorem is extended to log-Lipschitz subbundles with sharp regularity estimates.
result For log-Lipschitz involutive subbundles, there exists a homeomorphism with specific regularity properties.

We show that stochastic interpolation flow maps are Lipschitz with a sharp constant.

problem High dimensional sampling and transport problems.
method Investigating stochastic interpolation flow for generating data samples.
result Stochastic interpolation flow maps are Lipschitz with a sharp constant matching optimal transport maps.

The paper explores how close two Lipschitz functions can be without their difference exceeding a certain bound.

problem Understanding the closeness of two Lipschitz functions and their difference.
method Investigates the relationship between two Lip(γ)(\gamma) functions being close throughout a subset of their domain and the bound on the difference's Lipschitz norm.
result The Lipschitz norm of the difference between two functions is bounded by a small value when the distance to a subset is small.

The study proves a gap theorem for CAT(0) spaces with a constant below 1/(6√π).

problem Proving isoperimetric inequalities in non-positive curvature spaces.
method Introduced minimal tetrahedra to prove a linear inequality.
result Established a gap theorem for CAT(0) spaces with a constant below 1/(6√π).

New scalable Lipschitz bounds improve neural network robustness analysis.

problem Computing tight Lipschitz bounds for deep neural networks is challenging and computationally expensive.
method Derived new closed-form Lipschitz bounds using more general feasible points of LipSDP, avoiding SDP solvers.
result Improved scalability and precision of Lipschitz estimation for large neural networks.

We show that there is no bi-Lipschitz homeomorphism of R2\mathbb{R}^2 that maps a spiral with a sub-exponential decay of winding radii to an unwinded arc. This result is sharp as shows an example of a logarithmic spiral.

2016-03-10abs ↗pdf ↗

The paper calculates upper bounds on ReLU network Lipschitz constants.

problem Determining the maximum perturbation size for robustness of neural networks.
method Analyzing ReLU, affine-ReLU, and max pooling functions; combining results; tracking zero elements; using a computational approach.
result The method produces the largest known bounds on minimum adversarial perturbations for large networks.

Geodesically complete spaces with curvature bounded above have maps with finite energy that are Lipschitz.

problem Analyzing the properties of geodesically complete spaces with curvature constraints.
method Geometric perturbations of geodesics to curves with zero length on singular sets.
result Every Sobolev map in W1,W^{1,\infty} space has a Lipschitz representative with the same Lipschitz constant as its infinity energy.

Investigates Lipschitz continuity in neural networks across various settings.

problem Understanding the Lipschitz behavior of neural networks.
method Empirical investigation of Lipschitz bounds in different neural network architectures and datasets.
result Remarkable fidelity of the lower Lipschitz bound and a Double Descent trend in both upper and lower bounds.

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.

Efficient local Lipschitz bounds improve neural network robustness.

problem Certifying robustness of neural networks is challenging and often leads to over-regularization.
method Proposes an efficient trainable local Lipschitz upper bound by considering activation functions and weight matrices.
result Consistently outperforms state-of-the-art methods in clean and certified accuracy on various datasets.

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.

This paper bounds the Lipschitz constants of neural networks and their gradients.

problem Estimating the Lipschitz constant of complex models like neural networks.
method Local upper and lower bounds on Lipschitz constants computed with respect to network parameters.
result It is impossible to derive global upper bounds for the Lipschitz constants of neural networks.

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.

We examine the impact of learning Lipschitz continuous models in the context of model-based reinforcement learning. We provide a novel bound on multi-step prediction error of Lipschitz models where we quantify the error using the Wasserstein metric. We go on to prove an error bound for the value-function estimate arisi…

2018-04-19abs ↗pdf ↗

We provide convergence guarantees in Wasserstein distance for a variety of variance-reduction methods: SAGA Langevin diffusion, SVRG Langevin diffusion and control-variate underdamped Langevin diffusion. We analyze these methods under a uniform set of assumptions on the log-posterior distribution, assuming it to be smo…

2018-02-15abs ↗pdf ↗

The projection of a compact oriented submanifold M^{n-1} in R^{n+1} on a hyperplane P^{n} can fail to bound any region in P. We call this ``projecting to zero.'' Example: The equatorial S^1 in S^2 projects to zero in any plane containing the x_3-axis. Using currents to make this precise, we show: A lipschitz (homology)…

2002-09-18abs ↗pdf ↗

Sharp distance estimates for compact spin manifolds using Dirac operator.

problem Metric inequalities for compact spin manifolds with lower bounds on scalar and mean curvatures.
method Using the Dirac operator technique with spectral estimates and local boundary conditions.
result Optimal estimates for Riemannian bands and long neck problem solutions.

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.

Improved KL convergence bounds for score diffusion models without restrictive assumptions.

problem Lack of comprehensive quantitative results for diffusion models, especially in non-regular scores and estimators.
method Score diffusion models with fixed step size from Ornstein-Uhlenbeck and kinetic semigroups, providing explicit and sharp KL convergence bounds.
result Explicit and sharp convergence bounds in KL applicable to any data distribution with finite Fisher information.