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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…

Machine Learning · Statistics 2009-12-04 Marco Cuturi

Motivated by practical applications, I present a novel and comprehensive framework for operator-valued positive definite kernels. This framework is applied to both operator theory and stochastic processes. The first application focuses on…

Statistics Theory · Mathematics 2025-11-04 Saeed Hashemi Sababe

Hierarchical data pervades diverse machine learning applications, including natural language processing, computer vision, and social network analysis. Hyperbolic space, characterized by its negative curvature, has demonstrated strong…

Artificial Intelligence · Computer Science 2026-03-13 Leping Si , Meimei Yang , Hui Xue , Shipeng Zhu , Pengfei Fang

We study the Virasoro conformal block decomposition of the genus two partition function of a two-dimensional CFT by expanding around a Z3-invariant Riemann surface that is a three-fold cover of the Riemann sphere branched at four points,…

High Energy Physics - Theory · Physics 2018-12-05 Minjae Cho , Scott Collier , Xi Yin

This paper is devoted to the study of reproducing kernel Hilbert spaces. We focus on multipliers of reproducing kernel Banach and Hilbert spaces. In particular we tried to extend this concept and prove some theorems.

Functional Analysis · Mathematics 2016-01-07 Ali Ebadian , Saeed Hashemi Sababe , Maysam Zallaghi

We introduce a nonabelianization map for conformal blocks, which relates $c=1$ Virasoro blocks on a Riemann surface $C$ to Heisenberg blocks on a branched double cover $\widetilde{C}$ of $C$. The nonabelianization map uses the datum of a…

High Energy Physics - Theory · Physics 2025-12-16 Qianyu Hao , Andrew Neitzke

We develop a recursive approach to computing Neveu-Schwarz conformal blocks associated with n-punctured Riemann surfaces. This work generalizes the results of [1] obtained recently for the Virasoro algebra. The method is based on the…

High Energy Physics - Theory · Physics 2018-09-26 V. A. Belavin , R. V. Geiko

A new test for structural changes in functional data is investigated. It is based on Hilbert space theory and critical values are deduced from bootstrap iterations. Thus a new functional central limit theorem for the block bootstrap in a…

Statistics Theory · Mathematics 2015-09-16 Olimjon Sharipov , Johannes Tewes , Martin Wendler

We derive expressions for the Virasoro OPE and four-point conformal blocks on the sphere via the resolution of identity recently determined in [Phys. Rev. D 111, 085010 (2025), arXiv:2409.12224]. Even though the resolution of the identity…

High Energy Physics - Theory · Physics 2025-09-15 Jean-François Fortin , Lorenzo Quintavalle , Witold Skiba

We consider the problem of learning a set from random samples. We show how relevant geometric and topological properties of a set can be studied analytically using concepts from the theory of reproducing kernel Hilbert spaces. A new kind of…

Machine Learning · Statistics 2014-11-26 Ernesto De Vito , Lorenzo Rosasco , Alessandro Toigo

The theory of positive kernels and associated reproducing kernel Hilbert spaces, especially in the setting of holomorphic functions, has been an important tool for the last several decades in a number of areas of complex analysis and…

Operator Algebras · Mathematics 2016-02-03 Joseph A. Ball , Gregory Marx , Victor Vinnikov

We develop techniques useful for obtaining conformal blocks in embedding space. We construct a unique differential operator in embedding space and use it to construct a function that will be an important ingredient in assembling conformal…

High Energy Physics - Theory · Physics 2019-07-25 Jean-François Fortin , Witold Skiba

Representing images by compact codes has proven beneficial for many visual recognition tasks. Most existing techniques, however, perform this coding step directly in image feature space, where the distributions of the different classes are…

Computer Vision and Pattern Recognition · Computer Science 2014-09-02 Mehrtash Harandi , Mathieu Salzmann

This expository article presents the approach to statistical machine learning based on reproducing kernel Hilbert spaces. The basic framework is introduced for scalar-valued learning and then extended to operator learning. Finally, learning…

Machine Learning · Computer Science 2025-09-24 Lorenzo Rosasco

We study a reproducing kernel Hilbert space of functions defined on the positive integers and associated to the binomial coefficients. We introduce two transforms, which allow us to develop a related harmonic analysis in this Hilbert space.…

Complex Variables · Mathematics 2014-12-19 Daniel Alpay , Palle Jorgensen

The paper discusses a series of results concerning reproducing kernel Hilbert spaces, related to the factorization of their kernels. In particular, it is proved that for a large class of spaces isometric multipliers are trivial. One also…

Functional Analysis · Mathematics 2016-05-10 Rani Kumari , Jaydeb Sarkar , Srijan Sarkar , Dan Timotin

We study the Virasoro constraints for moduli spaces of representations of quiver with relations by Joyce's vertex algebras. Using the framed Virasoro constraints, we construct a representation of half of the Virasoro algebra on the…

Algebraic Geometry · Mathematics 2024-03-26 Woonam Lim , Miguel Moreira

We study linear integro-differential equations in Hilbert spaces with operator-valued kernels and give sufficient conditions for the well-posedness. We show that several types of integro-differential equations are covered by the class of…

Analysis of PDEs · Mathematics 2016-04-05 Sascha Trostorff

The quantum super-algebra structure on the deformed super Virasoro algebra is investigated. More specifically we established the possibility of defining a non trivial Hopf super-algebra on both one and two-parameters deformed super Virasoro…

Mathematical Physics · Physics 2014-10-07 Mostafa Mansour

Fusion frames are a very active area of research today because of their myriad of applications in pure mathematics, applied mathematics, engineering, medicine, signal and image processing and much more. They provide a great flexibility for…