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We study positive definiteness of kernels $K(x,y)$ on two-point homogeneous spaces. As opposed to the classical case, which has been developed and studied in the existing literature, we allow the kernel to have an (integrable) singularity…

Classical Analysis and ODEs · Mathematics 2024-10-30 Dmitriy Bilyk , Peter Grabner

The use of kernels for nonlinear prediction is widespread in machine learning. They have been popularized in support vector machines and used in kernel ridge regression, amongst others. Kernel methods share three aspects. First, instead of…

Machine Learning · Statistics 2025-08-25 Patrick J. F. Groenen , Michael Greenacre

We complete our theory of weighted $L^p(w_1) \times L^q(w_2) \to L^r(w_1^{r/p} w_2^{r/q})$ estimates for bilinear bi-parameter Calder\'on--Zygmund operators under the assumption that $w_1 \in A_p$ and $w_2 \in A_q$ are bi-parameter weights.…

Classical Analysis and ODEs · Mathematics 2020-04-21 Emil Airta , Kangwei Li , Henri Martikainen , Emil Vuorinen

We consider the theory of regression on a manifold using reproducing kernel Hilbert space methods. Manifold models arise in a wide variety of modern machine learning problems, and our goal is to help understand the effectiveness of various…

Machine Learning · Statistics 2020-10-19 Andrew McRae , Justin Romberg , Mark Davenport

In this paper we prove some sharp weighted norm inequalities for the multi(sub)linear maximal function $\Mm$ introduced in \cite{LOPTT} and for multilinear Calder\'on-Zygmund operators. In particular we obtain a sharp mixed…

Classical Analysis and ODEs · Mathematics 2012-11-22 Wendolín Damián , Andrei K. Lerner , Carlos Pérez

In this paper, we introduce a new kernel function which differs from previous functions, and play an important role for generating a new design of primal-dual interior point algorithms for semidefinite linear complementarity problem. Its…

Numerical Analysis · Mathematics 2021-08-18 Nabila Abdessemed , Rachid Benacer , Naima Boudiaf

By using the vector-valued theory of singular integrals, we prove a Hardy--Littlewood--Sobolev inequality on product Hardy spaces $H^p_{\rm{prod}}$, which is a parallel result of the classical Hardy--Littlewood--Sobolev inequality. The same…

Functional Analysis · Mathematics 2026-01-29 Yiyu Tang

We combine recent developments on weakly symmetric pseudo--riemannian nilmanifolds with with geometric methods for construction of unitary representations on square integrable Dolbeault cohomology spaces. This runs parallel to construction…

Representation Theory · Mathematics 2019-09-17 Joseph A. Wolf

Kernel matrices are a key quantity in kernel-based approximation, and important properties such as stability and algorithmic convergence can be analyzed with their help. In this work we refine a multivariate Ingham-type theorem, which is…

Numerical Analysis · Mathematics 2025-07-28 Tizan Wenzel , Armin Iske

We study how maximal regularity estimates with respect to the continuous functions improve automatically in cases where the spatial norm is fundamentally different from the supremum norm. More precisely, we invoke properties such as weak…

Functional Analysis · Mathematics 2026-05-14 Philip Preußler , Felix L. Schwenninger

The Calder\'on problem for the fractional Schr\"odinger equation was introduced in the work \cite{GSU}, which gave a global uniqueness result also in the partial data case. This article improves this result in two ways. First, we prove a…

Analysis of PDEs · Mathematics 2020-02-17 Angkana Rüland , Mikko Salo

The aim of this paper is to develop a theory of finite transformation monoids and in particular to study primitive transformation monoids. We introduce the notion of orbitals and orbital digraphs for transformation monoids and prove a…

Combinatorics · Mathematics 2010-04-20 Benjamin Steinberg

Given a finite subset $F$ of integer points in $\mathbb Z^d$, it is of interest to seek conditions on $F$ that allow it to multi-tile $\mathbb Z^d$ by translations. To this end, we give a discretized version of the Bombieri-Siegel formula,…

Number Theory · Mathematics 2024-09-17 Michel Faleiros Martins , Sinai Robins

In this article, we study properties of multilinear Fourier integral operators on weighted modulation spaces. In particular, using the theory of Gabor frames, we study boundedness of multilinear Fourier integral operators on products of…

Functional Analysis · Mathematics 2023-02-22 Aparajita Dasgupta , Lalit Mohan , Shyam Swarup Mondal

The problem of learning from seismic recordings has been studied for years. There is a growing interest in developing automatic mechanisms for identifying the properties of a seismic event. One main motivation is the ability have a reliable…

Machine Learning · Computer Science 2018-07-04 Ofir Lindenbaum , Yuri Bregman , Neta Rabin , Amir Averbuch

Akemann, Ipsen and Kieburg recently showed that the squared singular values of products of M rectangular random matrices with independent complex Gaussian entries are distributed according to a determinantal point process with a correlation…

Mathematical Physics · Physics 2015-06-16 Arno B. J. Kuijlaars , Lun Zhang

We extend to the framework of locally $L^0$-convex modules some results from classical convex analysis. Namely, randomized versions of Mazur lemma and Krein-Smulian theorem under mild stability properties are provided.

Functional Analysis · Mathematics 2017-06-20 Jose Miguel Zapata

We establish the full quasi-Banach range of $L^{p_1}(\mathbb R) \times L^{p_2}(\mathbb R) \rightarrow L^p(\mathbb R)$ bounds for one-dimensional bilinear singular integral operators with homogeneous kernels whose restriction $\Omega$ to the…

Classical Analysis and ODEs · Mathematics 2025-03-14 Petr Honzík , Stefanos Lappas , Lenka Slavíková

In this article we explain the theory of rigid residue complexes in commutative algebra and algebraic geometry, summarizing the background, recent results and anticipated future results. Unlike all previous approaches to Grothendiec…

Algebraic Geometry · Mathematics 2021-02-02 Amnon Yekutieli

Modern Bayesian optimization and adaptive sampling methods increasingly rely on nonlinear parametric models, yet theoretical guarantees for such models under adaptive data collection remain limited. Existing analyses largely focus on…

Machine Learning · Statistics 2026-05-14 Rafael Oliveira