arXiv research

New bounds for KRR condition number reveal overfitting phenomena.

Study on U-statistics with heavy-tailed samples, providing tail bounds and LDP.

Constructs index for elliptic operators using rapidly decaying kernels.

New method for spectral and Bergman kernels under local spectral gap condition.

We consider a model for linear transient price impact for multiple assets that takes cross-asset impact into account. Our main goal is to single out properties that need to be imposed on the decay kernel so that the model admits well-behaved optimal trade execution strategies. We first show that the existence of such s…

Study on biharmonic heat equation on manifolds with curvature constraints.

Parameterized state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the black box nature of such models it is important to understand the dynamical features of the input driving time series that are formed in the sta…

Given two sets of independent samples from unknown distributions $P$ and $Q$, a two-sample test decides whether to reject the null hypothesis that $P=Q$. Recent attention has focused on kernel two-sample tests as the test statistics are easy to compute, converge fast, and have low bias with their finite sample estimate…

Unified analysis of kernel-based and locally adaptive bandit optimization methods.

Study analyzes learnability of RKHS under L∞ norm for kernel methods.

We characterize the asymptotic performance of nonparametric goodness of fit testing. The exponential decay rate of the type-II error probability is used as the asymptotic performance metric, and a test is optimal if it achieves the maximum rate subject to a constant level constraint on the type-I error probability. We …

We study the asymptotic behavior of the generalized Bergman kernel of the renormalized Bochner-Laplacian on high tensor powers of a positive line bundle on a symplectic manifold of bounded geometry. First, we establish the off-diagonal exponential estimate for the generalized Bergman kernel. As an application, we obtai…

The Tick library simulates and learns Hawkes processes with latency effects.

We show that under very general assumptions the partial Bergman kernel function of sections vanishing along an analytic hypersurface has exponential decay in a neighborhood of the vanishing locus. Considering an ample line bundle, we obtain a uniform estimate of the Bergman kernel function associated to a singular metr…

We develop heat kernel and Green's function estimates for manifolds with positive bottom spectrum. The results are then used to establish existence and sharp estimates of the solution to the Poisson equation on such manifolds with Ricci curvature bounded below. As an application, we show that the curvature of a steady …

Explicit formula for Bergman kernel of abelian varieties proved.

The study analyzes prediction errors in systems with memory kernels, providing bounds and stability results.

Global stability proved for Navier-Stokes equations on hyperbolic space.

We give a short proof of a strong version of the short time asymptotic expansion of heat kernels associated to Laplace type operators acting on sections of vector bundles over compact Riemannian manifolds, including exponential decay of the difference of the approximate heat kernel and the true heat kernel. We use this…

Paper analyzes SGD in kernel regression, showing it outperforms offline methods.

Two new algorithms reduce online kernel regression's computational cost while maintaining optimal regret bounds.

We discover scaling laws for kernel regression loss under various learning rate schedules.

This paper studies the optimality of kernel methods in high-dimensional data clustering. Recent works have studied the large sample performance of kernel clustering in the high-dimensional regime, where Euclidean distance becomes less informative. However, it is unknown whether popular methods, such as kernel k-means, …

LOBRM model recreates limit order books from trade and quote data.

Positive definite kernels and their associated Reproducing Kernel Hilbert Spaces provide a mathematically compelling and practically competitive framework for learning from data. In this paper we take the approximation theory point of view to explore various aspects of smooth kernels related to their inferential proper…

New quantization methods improve accuracy of Random Fourier Features.

New stability theory for Sinkhorn semigroups with explicit decay rates.

A random walk $w_n$ on a separable, geodesic hyperbolic metric space $X$ converges to the boundary $\partial X$ with probability one when the step distribution supports two independent loxodromics. In particular, the random walk makes positive linear progress. Progress is known to be linear with exponential decay when …

Paper shows existence of vortex solutions with specific decay properties.

We show that the probability that a finitely supported random walk on a non-elementary subgroup of the the mapping class group gives a non-pseudo-Anosov element decays exponentially in the length of the random walk. More generally, we show that if R is a set of mapping class group elements with an upper bound on their …

In this paper, we examine the dependence of standard gluing process for pseudoholomorphic curves under the change of the length $T$ of the neck-region with respect to the cylindrical metrics associated to the given analytic coordinates near the punctures in the setting of bordered open Riemann surface with boundary pun…

Study Hilbert's projective metric for bounded growth functions leading to Sinkhorn's algorithm convergence.

This work studies nonnegativity-preserving kernels for stochastic equations and their applications.

We prove exponential decay of correlations for Hölder continuous observables with respect to any Gibbs measure for contact Anosov flows admitting Pesin sets with exponentially small tails. This is achieved by establishing strong spectral estimates for certain Ruelle transfer operators for such flows.

AdamNX improves Adam's stability by adjusting its learning rate.

Paper calculates eigenvalue decay rates for neural network kernels on general domains.

Kernel Density Estimation is a very popular technique of approximating a density function from samples. The accuracy is generally well-understood and depends, roughly speaking, on the kernel decay and local smoothness of the true density. However concrete statements in the literature are often invoked in very specific …

Laplace kernel and Neural Tangent Kernels are shown to be nearly identical for normalized data.

A new accelerated method with simpler momentum update rules.

Unified framework for analyzing gradient flows of measures with exponential decay of entropy.

Estimates kernel eigenvalues for compositional dot-product kernels.

Study on Wasserstein gradient flow for MMD between Coulomb measures.

New exponential decay estimate for Hermitian Yang-Mills metrics near branch points.

Estimates covariance matrices with correlations between samples.

We consider Hitchin's hyperkähler metric $g_{L^2}$ on the $SU(n)$-Hitchin moduli space moduli space over a compact Riemann surface. We prove that the difference between the metric $g_{L^2}$ and a simpler "semiflat" hyperkähler metric $g_{\mathrm{sf}}$ is exponentially-decaying along generic rays in the Hitchin moduli s…

In this paper we continue our study of the Laplacian on manifolds with axial analytic asymptotically cylindrical ends initiated in~arXiv:1003.2538. By using the complex scaling method and the Phragmén-Lindelöf principle we prove exponential decay of the eigenfunctions corresponding to the non-threshold eigenvalues of t…

The goal of this paper is to prove a result conjectured in Föllmer and Schachermayer [FS07], even in slightly more general form. Suppose that S is a continuous semimartingale and satisfies a large deviations estimate; this is a particular growth condition on the mean-variance tradeoff process of S. We show that S then …

Active data collection improves convergence rates in operator learning.