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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.

168,694 papers · 148 categories

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64127191254 · Jun 202019922001200920172026
48 results for unbounded domain

Study visibility properties of Kobayashi distance on unbounded domains.

problem Understanding visibility properties of Kobayashi distance on unbounded domains.
method Analyzing visibility properties in the context of Kobayashi hyperbolic domains, focusing on unbounded domains and their boundary behavior.
result Carathéodory-type extension theorem for biholomorphisms between planar domains, including infinitely-connected domains.

The paper solves the Dirichlet problem for minimal surfaces on unbounded helicoidal domains.

problem Solving the Dirichlet problem for minimal surfaces on unbounded helicoidal domains.
method Analyzes GG-invariant solutions in C2,αC^{2,α} domains with helicoidal projections.
result Existence of GG-invariant solutions with controlled gradient at infinity.

New algorithms for online learning without boundedness or Lipschitz loss assumptions.

problem Online learning with unbounded domains and non-Lipschitz losses.
method Developed an algorithm with a specific regret bound and used it for saddle-point optimization.
result First algorithm achieving non-trivial dynamic regret in an unbounded domain for non-Lipschitz losses.

For a domain ΩRnΩ\subset\mathbb R^n, we introduce the concept of a uniformly CmC^m defining function. We characterize uniformly CmC^m defining functions in terms of the signed distance function for the boundary and provide a large class of examples of unbounded domains with uniformly CmC^m defining functions. Some of ou…

2011-11-17abs ↗pdf ↗

New neural network rates for unbounded domains with weighted Sobolev spaces.

problem Improving neural network approximation rates for unbounded domains.
method Embedding results for weighted Fourier-Lebesgue spaces in weighted Sobolev spaces, followed by asymptotic approximation rates.
result Asymptotic approximation rates for shallow neural networks without curse of dimensionality for unbounded domains and Muckenhoupt weights.

We introduce a new class of unbounded model subdomains of C2\mathbb{C}^2 for the b\Box_b problem. Unlike previous finite type models, these domains need not be bounded by algebraic varieties. In this paper we obtain precise global estimates for the Carnot-Carathéodory metric induced on the boundary of such domains by …

2013-12-03abs ↗pdf ↗

We consider smooth radial solutions to the Hamiltonian stationary equation which are defined away from the origin. We show that in dimension two all radial solutions on unbounded domains must be special Lagrangian. In contrast, for all higher dimensions there exist non-special Lagrangian radial solutions over unbounded…

2016-12-08abs ↗pdf ↗

The Fock-Bargmann-Hartogs domain Dn,m(μ)D_{n,m}(μ) (μ>0μ>0) in Cn+m\mathbf{C}^{n+m} is defined by the inequality w2<eμz2,\|w\|^2<e^{-μ\|z\|^2}, where (z,w)Cn×Cm(z,w)\in \mathbf{C}^n\times \mathbf{C}^m, which is an unbounded non-hyperbolic domain in Cn+m\mathbf{C}^{n+m}. Recently, Yamamori gave an explicit formula for the Bergman kernel of the…

2014-12-11abs ↗pdf ↗

The paper proves a conjecture about the Bergman metric of real analytic domains.

problem Proving the Cheng-Yau conjecture for real analytic pseudoconvex domains.
method Localization of Bergman kernels, extension theorem, and Einstein metrics.
result The Bergman metric of a bounded pseudoconvex domain with real-analytic boundary is Einstein if and only if the domain is biholomorphic to the unit ball.

New algorithms solve stochastic variational inequalities without bounded variance assumption.

problem Solving stochastic variational inequalities without bounded variance assumption.
method Developed algorithms for two classes of problems: monotone and structured nonmonotone VIs.
result Oracle complexity of O(ε^-4) for solving VIs with unbounded domains and possibly unbounded variance.

New analysis shows a gap between Gaussian RKHS and neural networks on unbounded domains.

problem Understanding the function space bias of neural networks compared to Gaussian RKHS.
method Infinite-center asymptotic analysis of neural network Banach space and Gaussian RKHS on unbounded domains.
result Certain functions in Gaussian RKHS have infinite norm in neural network Banach space on unbounded domains.

Paper develops estimators for unbounded density ratios with applications in error control.

problem Estimating density ratios with unbounded domains and ranges.
method Least squares and logistic regression loss functions for density ratio estimation.
result Established upper bounds on estimation errors with optimal rates for unbounded density ratios.

We classify the tube domains in C^4 with affinely homogeneous base whose boundary contains a non-degenerate affinely homogeneous hypersurface. It follows that these domains are holomorphically homogeneous and amongst them there are four new examples of unbounded homogeneous domains (that do not have bounded realisation…

2004-08-09abs ↗pdf ↗

New algorithms achieve high-probability parameter-free regret in online convex optimization with heavy-tailed data.

problem Achieving high-probability parameter-free regret in online convex optimization with heavy-tailed data.
method Developed new regularization techniques to handle exponentially large iterates and heavy-tailed subgradients.
result Achieved regret bound of O(uT1/plog(1/δ))O(\| \mathbf{u} \| T^{1/\mathfrak{p}} \log (1/δ)) with high probability for subgradients with bounded pthp^{th} moments.

Algorithm learns diffusion processes with high-dimensional state spaces.

problem Stochastic control of unbounded diffusion processes with high-dimensional state spaces.
method Adaptive partitioning and learning algorithm that refines discretization based on estimation bias and statistical confidence.
result Established regret bounds that depend on problem parameters, extending to unbounded diffusion processes.

Deep neural networks classify unbounded Gaussian mixture data without dimensionality issues.

problem Binary classification of unbounded Gaussian mixture data.
method Deep ReLU neural networks with non-asymptotic upper bounds and convergence rates.
result Deep ReLU networks can classify unbounded Gaussian mixture data without dimensionality constraints.

The paper examines geometric properties of domains for the p-Laplacian in Euclidean and hyperbolic spaces.

problem Exploring geometric properties of unbounded extremal domains for the p-Laplacian operator.
method Analyzing properties in Euclidean and hyperbolic spaces, proving constraints on domains and their asymptotic boundaries.
result Extremal domains in two dimensions must be balls, and in hyperbolic space, they have specific geometric constraints.

We study the minimal surface equation in the Heisenberg space, Nil_3. A geometric proof of non existence of minimal graphs over non convex, bounded and unbounded domains is achieved (our proof holds in the Euclidean space as well). We solve the Dirichlet problem for the minimal surface equation over bounded and unbound…

2015-08-07abs ↗pdf ↗

The Einstein/Abelian-Yang-Mills Equations reduce in the stationary and axially symmetric case to a harmonic map with prescribed singularities $\p\colon\R^3\smΣ\to\H^{k+1}_\C$ into the (k+1)(k+1)-dimensional complex hyperbolic space. In this paper, we prove the existence and uniqueness of harmonic maps with prescribed sing…

1995-09-19abs ↗pdf ↗

The existence and nonexistence of λλ-harmonic functions in unbounded domains of Hn\mathbb{H}^n are investigated. We prove that if the (n1)/2(n-1)/2 Hausdorff measure of the asymptotic boundary of a domain ΩΩ is zero, then there is no bounded λλ-harmonic function of ΩΩ for λ[0,λ1(Hn)]λ\in [0,λ_1(\mathbb{H}^n)], where $λ_1(\mathb…

2015-12-04abs ↗pdf ↗

Study equi-affine invariants for convex domains with asymptotes.

problem Understanding geometric properties of convex domains with specific asymptotes.
method Introducing equi-affine invariants by averaging tropical structures.
result Proving a limiting description of level sets for unbounded domains with two non-parallel asymptotes.

The Fock-Bargmann-Hartogs domain Dn,mD_{n,m} in Cn+m\mathbb{C}^{n+m} is defined by the inequality w2<ez2,\|w\|^2<e^{-\|z\|^2}, where (z,w)Cn×Cm(z,w)\in \mathbb{C}^n\times \mathbb{C}^m, which is an unbounded non-hyperbolic domain in Cn+m\mathbb{C}^{n+m}. This paper mainly consists of three parts. Firstly, we give the explicit expression o…

2018-12-18abs ↗pdf ↗

Self-training improves gradual domain adaptation with unlabeled data.

problem Improving machine learning models' adaptability to gradually shifting data distributions.
method Proved upper bounds on self-training error, highlighted the importance of regularization and label sharpening, and demonstrated algorithmic insights.
result Self-training works well for gradual shifts, especially with small Wasserstein-infinity distance.