arXiv research

MKLpy simplifies Multiple Kernel Learning in Python.

The Bergman-Szegő kernel is analyzed for weakly pseudoconvex CR manifolds of finite type.

The study shows algebraic Bergman kernels imply finite type boundaries in complex domains.

Paper characterizes embeddability of function spaces into $L_p$-type RKBS via metric entropy.

We study inequalities related to the heat kernel for the hypoelliptic sublaplacian on an H-type Lie group. Specifically, we obtain precise pointwise upper and lower bounds on the heat kernel function itself. We then apply these bounds to derive an estimate on the gradient of solutions of the heat equation, which is kno…

This paper studies neural network operators and their convergence properties.

Study Brownian motions and heat kernel bounds on Kähler and quaternion Kähler manifolds.

Kernel smoothing on unknown manifolds with bounds and asymptotic normality.

In this paper, we study how to get the Ricci expanders from W+-functional through the heat kernel estimate of the conjugate heat equation to the type III singularity of Ricci flow. The Gaussian upper and lower bounds are established for the related heat kernel in accordance to the interesting work of Cao-Zhang for the …

Study Bergman kernels on complex hyperbolic cusps, generalizing previous results.

A novel nonstationary permanental process relaxes kernel constraints and captures complex data patterns.

The term "CoRE kernel" stands for correlation-resemblance kernel. In many applications (e.g., vision), the data are often high-dimensional, sparse, and non-binary. We propose two types of (nonlinear) CoRE kernels for non-binary sparse data and demonstrate the effectiveness of the new kernels through a classification ex…

New test for conditional independence using kernel embeddings.

We prove a Davies type double integral estimate for the heat kernel $H(y,t;x,l)$ under the Ricci flow. As a result, we give an affirmative answer to a question proposed by Chow etc.. Moreover, we apply the Davies type estimate to provide a new proof of the Gaussian upper and lower bounds of $H(y,t;x,l)$ which were firs…

Kernel Estimation is one of the most widely used estimation methods in non-parametric Statistics, having a wide-range of applications, including spot volatility estimation of stochastic processes. The selection of bandwidth and kernel function is of great importance, especially for the finite sample settings commonly e…

Sharp gradient estimate on hyperbolic spaces derived from heat kernel.

The paper provides concentration bounds for kernel matrix eigenvalues.

We prove the following gradient inequality for the subelliptic heat kernel on nilpotent Lie groups $G$ of H-type: $|\nabla P_t f| \le K P_t(|\nabla f|)$ where $P_t$ is the heat semigroup corresponding to the sublaplacian on $G$, $\nabla$ is the subelliptic gradient, and $K$ is a constant. This extends a result of H.-…

New kernels defined for various spaces, including measures.

The paper extends Ricci flow theory with Type-I scalar curvature bounds, proving entropy convergence and characterizing singular sets.

We establish precise upper and lower bounds for the subelliptic heat kernel on nilpotent Lie groups $G$ of H-type. Specifically, we show that there exist positive constants $C_1$, $C_2$ and a polynomial correction function $Q_t$ on $G$ such that $C_1 Q_t e^{-\frac{d^2}{4t}} \le p_t \le C_2 Q_t e^{-\frac{d^2}{4t}}$ wh…

Kernel-based learning predicts ICU escalation from COVID-19 chest X-rays.

Study detects P-type bifurcations in single system realizations using unreliable kernel density estimates.

Energy statistics was proposed by Sz\' ekely in the 80's inspired by Newton's gravitational potential in classical mechanics and it provides a model-free hypothesis test for equality of distributions. In its original form, energy statistics was formulated in Euclidean spaces. More recently, it was generalized to metric…

A new framework improves kernel Stein discrepancy tests for validating distributions.

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…

Framework for joint inference of network topology and interaction types in heterogeneous systems.

Develops multifactor approximations for SVEs with completely monotone kernels.

We consider the heat kernel (and the zeta function) associated with Laplace type operators acting on a general irreducible rank 1 locally symmetric space X. The set of Minakshisundaram- Pleijel coefficients {A_k(X)}_{k=0}^{\infty} in the short-time asymptotic expansion of the heat kernel is calculated explicitly.

Extends heat kernel estimates for super Ricci flow.

This paper studies Brownian motion and heat kernel measure on a class of infinite dimensional Lie groups. We prove a Cameron-Martin type quasi-invariance theorem for the heat kernel measure and give estimates on the $L^p$ norms of the Radon-Nikodym derivatives. We also prove that a logarithmic Sobolev inequality holds …

We provide a unifying framework linking two classes of statistics used in two-sample and independence testing: on the one hand, the energy distances and distance covariances from the statistics literature; on the other, maximum mean discrepancies (MMD), that is, distances between embeddings of distributions to reproduc…

Study uses G-BSDEs to decompose pricing kernels under robust G-expectation.

MCNO learns PDE solution operators using Monte Carlo sampling.

This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functions $\{k(x…

MixCIT tests conditional independence for mixed data types efficiently and reliably.

Computed BNSR-invariants of Houghton groups, confirming a conjecture.

Paper proves non-triviality of Johnson kernel torsion subgroup.

Extends Tanimoto kernel to real-valued functions.

Two-dimensional domains with Kähler-Einstein Bergman metrics are biholomorphic to the unit ball.

Two-sample tests using MMD control type I error and achieve optimal power.

We study the heat kernel asymptotics for the Laplace type differential operators on vector bundles over Riemannian manifolds. In particular this includes the case of the Laplacians acting on differential p-forms. We extend our results obtained earlier for the scalar Laplacian and present closed formulas for all heat in…

Study Dirac operators on incomplete cusp edge spaces, proving self-adjointness and Fredholm properties.

Survey of kernels, RKHS, and their applications in machine learning.

We study the subelliptic heat kernels of the CR three dimensional solvable Lie groups. We first classify all left-invariant sub-Riemannian structures on three dimensional solvable Lie groups and obtain representations of these groups. We give expressions for the heat kernels on these groups and obtain heat semigroup gr…

This article shows that if the negative part of Ricci curvature lies in the Kato class, the heat kernel satisfies a Li-Yau type estimate. Additionally, using the resulting heat kernel bound, we show that the obtained heat kernel estimate leads to bounds on the first Betti number only depending on the Kato constant.

New insights into CI tests reveal key factors for practical performance.

In this paper we study the variational problem associated to support vector regression in Banach function spaces. Using the Fenchel-Rockafellar duality theory, we give explicit formulation of the dual problem as well as of the related optimality conditions. Moreover, we provide a new computational framework for solving…