Positive operator-valued kernels and non-commutative probability
- URL: http://arxiv.org/abs/2405.09315v1
- Date: Wed, 15 May 2024 13:16:11 GMT
- Title: Positive operator-valued kernels and non-commutative probability
- Authors: Palle E. T. Jorgensen, James Tian,
- Abstract summary: We prove new factorization and dilation results for general positive operator-valued kernels.
We present their implications for associated Hilbert space-valued Gaussian processes.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We prove new factorization and dilation results for general positive operator-valued kernels, and we present their implications for associated Hilbert space-valued Gaussian processes, and their covariance structure. Further applications are to non-commutative probability theory, including a non-commutative Radon--Nikodym theorem for systems of completely positive maps.
Related papers
- A Mathematical Analysis of Neural Operator Behaviors [0.0]
This paper presents a rigorous framework for analyzing the behaviors of neural operators.
We focus on their stability, convergence, clustering dynamics, universality, and generalization error.
We aim to offer clear and unified guidance in a single setting for the future design of neural operator-based methods.
arXiv Detail & Related papers (2024-10-28T19:38:53Z) - Neural Operators with Localized Integral and Differential Kernels [77.76991758980003]
We present a principled approach to operator learning that can capture local features under two frameworks.
We prove that we obtain differential operators under an appropriate scaling of the kernel values of CNNs.
To obtain local integral operators, we utilize suitable basis representations for the kernels based on discrete-continuous convolutions.
arXiv Detail & Related papers (2024-02-26T18:59:31Z) - Posterior Contraction Rates for Mat\'ern Gaussian Processes on
Riemannian Manifolds [51.68005047958965]
We show that intrinsic Gaussian processes can achieve better performance in practice.
Our work shows that finer-grained analyses are needed to distinguish between different levels of data-efficiency.
arXiv Detail & Related papers (2023-09-19T20:30:58Z) - Deep Learning with Kernels through RKHM and the Perron-Frobenius
Operator [14.877070496733966]
Reproducing kernel Hilbert $C*$-module (RKHM) is a generalization of reproducing kernel Hilbert space (RKHS) by means of $C*$-algebra.
We derive a new Rademacher generalization bound in this setting and provide a theoretical interpretation of benign overfitting by means of Perron-Frobenius operators.
arXiv Detail & Related papers (2023-05-23T01:38:41Z) - Gaussian Processes on Distributions based on Regularized Optimal
Transport [2.905751301655124]
We present a novel kernel over the space of probability measures based on the dual formulation of optimal regularized transport.
We prove that this construction enables to obtain a valid kernel, by using the Hilbert norms.
We provide theoretical guarantees on the behaviour of a Gaussian process based on this kernel.
arXiv Detail & Related papers (2022-10-12T20:30:23Z) - Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge
Equivariant Projected Kernels [108.60991563944351]
We present a recipe for constructing gauge equivariant kernels, which induce vector-valued Gaussian processes coherent with geometry.
We extend standard Gaussian process training methods, such as variational inference, to this setting.
arXiv Detail & Related papers (2021-10-27T13:31:10Z) - Neural Operator: Learning Maps Between Function Spaces [75.93843876663128]
We propose a generalization of neural networks to learn operators, termed neural operators, that map between infinite dimensional function spaces.
We prove a universal approximation theorem for our proposed neural operator, showing that it can approximate any given nonlinear continuous operator.
An important application for neural operators is learning surrogate maps for the solution operators of partial differential equations.
arXiv Detail & Related papers (2021-08-19T03:56:49Z) - Generalization Properties of Stochastic Optimizers via Trajectory
Analysis [48.38493838310503]
We show that both the Fernique-Talagrand functional and the local powerlaw are predictive of generalization performance.
We show that both our Fernique-Talagrand functional and the local powerlaw are predictive of generalization performance.
arXiv Detail & Related papers (2021-08-02T10:58:32Z) - Advanced Stationary and Non-Stationary Kernel Designs for Domain-Aware
Gaussian Processes [0.0]
We propose advanced kernel designs that only allow for functions with certain desirable characteristics to be elements of the reproducing kernel Hilbert space (RKHS)
We will show the impact of advanced kernel designs on Gaussian processes using several synthetic and two scientific data sets.
arXiv Detail & Related papers (2021-02-05T22:07:56Z) - Mat\'ern Gaussian processes on Riemannian manifolds [81.15349473870816]
We show how to generalize the widely-used Mat'ern class of Gaussian processes.
We also extend the generalization from the Mat'ern to the widely-used squared exponential process.
arXiv Detail & Related papers (2020-06-17T21:05:42Z) - Analysis via Orthonormal Systems in Reproducing Kernel Hilbert
$C^*$-Modules and Applications [12.117553807794382]
We propose a novel data analysis framework with reproducing kernel Hilbert $C*$-module (RKHM)
We show the theoretical validity for the construction of orthonormal systems in Hilbert $C*$-modules, and derive concrete procedures for orthonormalization in RKHMs.
We apply those to generalize with RKHM kernel principal component analysis and the analysis of dynamical systems with Perron-Frobenius operators.
arXiv Detail & Related papers (2020-03-02T10:01:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.