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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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12.5%25.0%37.5%50.0% · Sep 199319922001200920172026
48 results for degenerate kernel

Study continuity of Bergman kernels on degenerating varieties.

problem Continuity and uniform convergence of Bergman kernels on degenerating varieties.
method Introduced fiberwise Bergman kernel for flat families of polarized varieties, established continuity and uniform convergence results.
result Uniform convergence of Fubini-Study currents and continuity of fiberwise Bergman kernel on test configurations.

A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on…

2014-08-23abs ↗pdf ↗

The paper solves heat kernel asymptotics on non-degenerate CR manifolds.

problem Existence of small-time asymptotics for the heat kernel of the Kohn Laplacian on CR manifolds.
method Analytic methods and spectral theory for CR manifolds.
result Established small-time asymptotics for the heat kernel and analytic torsion on non-degenerate CR manifolds.

Study on stability of 3D sessile drops, identifying degenerate kernel.

problem Linear stability of three-dimensional sessile drops with a free contact line.
method Derived constrained second variation, formulated Jacobi problem, combined geometric and Fourier analysis.
result Kernel of the constrained Jacobi operator is exactly the space of horizontal translations under pressure-volume nondegeneracy.

High-dimensional U-statistics show surprising phase transitions, impacting kernel-based tests.

problem Understanding phase transitions in high-dimensional U-statistics.
method Proved a convergence theorem for U-statistics of degree two in high dimensions.
result High-dimensional U-statistics can have non-Gaussian limits with larger variance and asymmetry.

We treat the Witten operator on the de Rham complex with semiclassical heat kernel methods to derive the Poincaré-Hopf theorem and degenerate generalizations of it. Thereby, we see how the semiclassical asymptotics of the Witten heat kernel are related to approaches using the Thom form of Mathai and Quillen.

2013-02-27abs ↗pdf ↗

Manifolds with fibered cusps are a class of complete noncompact Riemannian manifolds including all locally symmetric spaces of rank one. We study the spectrum of the Hodge Laplacian with coefficients in a flat bundle on a closed manifold undergoing degeneration to a manifold with fibered cusps. We obtain precise asympt…

2014-10-30abs ↗pdf ↗

Deep Gaussian processes can have non-degenerate and non-Gaussian limits.

problem Understanding the behavior of deep Gaussian processes as depth grows.
method Studying the limit of compositional Gaussian processes where each layer is a Gaussian process.
result Identified a sharp bandwidth threshold above which the limit is degenerate, and proved that for bandwidths below this threshold, the limit is a non-degenerate and non-Gaussian distribution.

Study gaps and clusters in eigenvalues of magnetic Laplacian on manifolds.

problem Understanding gaps and clusters in eigenvalues of magnetic Laplacian on manifolds.
method Analyzes high tensor powers of Hermitian line bundles with non degenerate curvature, proving Riemann-Roch numbers for eigenvalue clusters and describing spectral projectors.
result Clusters and gaps in eigenvalues are described by Riemann-Roch numbers and have pointwise kernel descriptions.

There are two parts of this paper. First, we discovered an explicit formula for the complex Hessian of the weighted log-Bergman kernel on a parallelogram domain, and utilised this formula to give a new proof about the strict convexity of the Mabuchi functional along a smooth geodesic. Second, when a C^{1,1}-geodesic co…

2017-11-27abs ↗pdf ↗

Paper analyzes soft tree ensembles using NTK, finding only leaf count matters.

problem Understanding impact of various tree architectures in ensemble learning.
method Formulated and analyzed Neural Tangent Kernel (NTK) for soft tree ensembles.
result Only the number of leaves at each depth is relevant for tree architecture in ensemble learning.

Study small eigenvalues of Riemann surfaces degenerating with Kähler metrics.

problem Determining small eigenvalues of the Laplacian on degenerating Riemann surfaces.
method Combining heat kernel estimates and Quillen metrics to compute asymptotic behavior of eigenvalues.
result Explicit calculation of small eigenvalues as a function of the parameter.

A new data-adaptive prior stabilizes kernel learning in operators.

problem Learning kernels in operators from data is ill-posed due to nonlocal dependence.
method Introduces a data-adaptive prior to stabilize the Bayesian posterior mean.
result The data-adaptive prior achieves a stable posterior with small noise limits.

Study Bergman and spectral kernels for non-compact complex manifolds.

problem Analyze asymptotic behavior of kernels over non-compact complex manifolds.
method Generalize scaling method to study Bergman and spectral kernels.
result Derive leading term of Bergman and spectral kernels under local convergence of Chern curvatures.

Let (M,J)(M,J) be a compact complex manifold of complex dimension mm and let gsg_s be a one-parameter family of Hermitian forms on MM that are smooth and positive definite for each fixed s(0,1]s\in (0,1] and that somehow degenerates to a Hermitian pseudometric hh for ss tending to 00. In this paper under rather general a…

2019-12-13abs ↗pdf ↗

The conormal lift of a link KK in R3\R^3 is a Legendrian submanifold ΛKΛ_K in the unit cotangent bundle UR3U^* \R^3 of R3\R^3 with contact structure equal to the kernel of the Liouville form. Knot contact homology, a topological link invariant of KK, is defined as the Legendrian homology of ΛKΛ_K, the homology of a di…

2011-09-07abs ↗pdf ↗

The paper studies hanging chains and surfaces in degenerate geometries.

problem Investigating hanging chains and surfaces in simply isotropic plane and space.
method Characterizing catenaries and proving them as minimal surfaces in the simply isotropic space.
result The simply isotropic catenary is the generating curve of a minimal surface of revolution.

Let φC(Cn)φ\in C^\infty(\Complex^n) be a given real valued function. We assume that $\pr\ddbarφ$ is non-degenerate of constant signature (n,n+)(n_-,n_+) on Cn\Complex^n. When q=nq=n_-, it is well-known that the Bergman kernel for (0,q)(0,q) forms with respect to the kk-th weight e2kφe^{-2kφ}, k>0k>0, admits a full asymptotic expansi…

2012-08-19abs ↗pdf ↗

Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL libraries with highly-specialized kernels for each workload/architecture, leading to numerous, complex code-bases that strive for performance, yet …

2019-06-15abs ↗pdf ↗

We study the spectrum and heat kernel of the Hodge Laplacian with coefficients in a flat bundle on a closed manifold degenerating to a manifold with wedge singularities. Provided the Hodge Laplacians in the fibers of the wedge have an appropriate spectral gap, we give uniform constructions of the resolvent and heat ker…

2018-07-05abs ↗pdf ↗

Paper develops efficient incomplete U-statistics for degenerate cases.

problem High computational cost and non-standard asymptotic behavior in degenerate U-statistics.
method Characterizes dependence structure using hypergraph theory and combinatorial designs, bypassing traditional Hoeffding decomposition.
result Derives a Berry-Esseen bound for incomplete U-statistics of deterministic designs, enabling Gaussian limiting distributions in degenerate cases.

Wide neural networks converge to Gaussian processes, improving generalization.

problem Understanding the generalization of wide neural networks, especially deep equilibrium models.
method Investigation of deep equilibrium models (DEQs) with infinite-depth layers, focusing on their convergence to Gaussian processes as width and depth approach infinity.
result Wide DEQs converge to Gaussian processes, maintaining generalization performance.

Permutation-valued features arise in a variety of applications, either in a direct way when preferences are elicited over a collection of items, or an indirect way in which numerical ratings are converted to a ranking. To date, there has been relatively limited study of regression, classification, and testing problems …

2016-03-25abs ↗pdf ↗

Let (X,T1,0X)(X, T^{1,0}X) be a compact connected orientable CR manifold of dimension 2n+12n+1 with non-degenerate Levi curvature. Assume that XX admits a connected compact Lie group action GG. Under certain natural assumptions about the group action GG, we show that the GG-invariant Szegö kernel for (0,q)(0,q) forms is a comp…

2017-02-16abs ↗pdf ↗

The Bergman kernels of holomorphic vector bundles are studied to extend the Fubini-Study map.

problem Extending the Fubini-Study map to a closed range for general inner products.
method Associate Bergman kernels with general inner products on the dual space.
result FS is an injective immersion but not necessarily closed in the space of positive definite inner products.

The abstract presents a new theorem using Ross-Witt Nyström correspondence and Berndtsson's theorem.

problem The abstract tackles the Ohsawa-Takegoshi extension theorem and its applications.
method The approach uses Ross-Witt Nyström correspondence and Berndtsson's theorem in \(\mathbb{C}^*\)-degeneration.
result The approach provides a quick proof of the Ohsawa-Takegoshi extension theorem without limits or singular weights.

We propose a Hodge theory for the spaces E2p,qE_2^{p,\,q} featuring at the second step either in the Frölicher spectral sequence of an arbitrary compact complex manifold XX or in the spectral sequence associated with a pair (N,F)(N,\,F) of complementary regular holomorphic foliations on such a manifold. The main idea is to …

2016-01-19abs ↗pdf ↗

New method for MMD with unequal sample sizes improves test power.

problem Existing MMD methods assume equal sample sizes, discarding valuable data.
method Extended generalized U-statistics to handle unequal sample sizes.
result New asymptotic distributions and power optimization for MMD with unequal sample sizes.

Improved estimation of higher order integrals using shrinkage techniques.

problem Estimating higher order Bochner integrals in non-parametric settings.
method Shrinkage of U-statistic towards a target element, considering kernel degeneracy.
result Consistent shrinkage estimators with fast rates of convergence, even for non-degenerate kernels.

This paper explains robust overfitting in wide DNNs using adversarial training and NTK theory.

problem Robust overfitting in adversarially trained wide DNNs.
method Theoretical analysis using neural tangent kernel (NTK) theory and adversarial training dynamics.
result Adversarial training can lead to robust overfitting in wide DNNs, which can be mitigated by the proposed Adv-NTK method.

A family of maximum mean discrepancy (MMD) kernel two-sample tests is introduced. Members of the test family are called Block-tests or B-tests, since the test statistic is an average over MMDs computed on subsets of the samples. The choice of block size allows control over the tradeoff between test power and computatio…

2013-07-08abs ↗pdf ↗

Paper develops methods for analyzing forms with synchronized singularities.

problem Analyzing forms with synchronized singularities.
method Exact reduction, analytic transfer, and geometric recomposition.
result Transfer of sparse domination principle to synchronized singular forms.

We obtain a vanishing theorem for the kernel of a Dirac operator on a Clifford module twisted by a sufficiently large power of a line bundle, whose curvature is non-degenerate at any point of the base manifold. In particular, if the base manifold is almost complex, we prove a vanishing theorem for the kernel of a $\spi…

1998-05-27abs ↗pdf ↗

Bayesian deep neural networks converge to processes with α-stable marginals under infinite variance weights.

problem Representation learning in deep kernel processes is hindered by deterministic covariance kernels.
method Showed convergence to α-stable processes with conditionally Gaussian representations in infinite-width networks.
result Conditional random covariance kernels can be recursively linked, even if the process is α-stable.

In this paper we define a new convergence called "asymptotically conic convergence" in which a smooth family of Riemannian metrics on a fixed compact manifold degenerate to a metric with isolated conic singularity. Our results are: convergence of the spectrum of the geometric Laplacians and uniform convergence of the c…

2007-01-13abs ↗pdf ↗