Imagen de Portada

Spectral Regularized Kernel Two-Sample Tests

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Hagrass, Omar, Sriperumbudur, Bharath K., Li, Bing
Formato:	Preprint
Publicado:	2022
Materias:	Statistics Theory Machine Learning Primary: 62G10, Secondary: 65J20, 65J22, 46E22, 47A52
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Spectral Regularized Kernel Goodness-of-Fit Tests
por: Hagrass, Omar, et al.
Publicado: (2023)

Minimax Optimal Goodness-of-Fit Testing with Kernel Stein Discrepancy
por: Hagrass, Omar, et al.
Publicado: (2024)

Minimax Optimal Kernel Two-Sample Tests with Random Features
por: Mukherjee, Soumya, et al.
Publicado: (2025)

Maximum a posteriori testing in statistical inverse problems
por: Kretschmann, Remo, et al.
Publicado: (2024)

Empirical Risk Minimization as Parameter Choice Rule for General Linear Regularization Methods
por: Li, Housen, et al.
Publicado: (2017)

Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems
por: Rastogi, Abhishake, et al.
Publicado: (2019)

Are minimizers of the Onsager-Machlup functional strong posterior modes?
por: Kretschmann, Remo
Publicado: (2022)

Optimal regularized hypothesis testing in statistical inverse problems
por: Kretschmann, Remo, et al.
Publicado: (2022)

Shorting Dynamics and Structured Kernel Regularization
por: Tian, James
Publicado: (2025)

Sparse variational regularization with oversmoothing penalty term in the scale of sequence spaces
por: Plato, Robert, et al.
Publicado: (2025)

Nyström Kernel Stein Discrepancy Tests
por: Kalinke, Florian, et al.
Publicado: (2026)

Nyström Kernel Stein Discrepancy
por: Kalinke, Florian, et al.
Publicado: (2024)

Non-asymptotic error estimates for the Laplace approximation in Bayesian inverse problems
por: Helin, Tapio, et al.
Publicado: (2020)

An Inverse Problems Approach to Pulse Wave Analysis in the Human Brain
por: Weissinger, Lukas, et al.
Publicado: (2024)

RKH spaces of Brownian type defined by Cesàro-Hardy operators
por: Galé, José E., et al.
Publicado: (2024)

Hybrid ABBA-GMRES for Unmatched Backprojectors in Large Scale X-Ray Computerized Tomography
por: Bentley, Ryan, et al.
Publicado: (2026)

The Stochastic Occupation Kernel (SOCK) Method for Learning Stochastic Differential Equations
por: Wells, Michael L., et al.
Publicado: (2025)

A new kernel-based index for the global sensitivity analysis of models with correlated inputs
por: Larsen, Troy, et al.
Publicado: (2026)

Regularization with optimal space-time priors
por: Bubba, Tatiana A., et al.
Publicado: (2024)

Deep neural network yields regularization for ill-posed inverse problems
por: Zhu, Qiao, et al.
Publicado: (2026)

Adaptive Nonparametric Estimation via Kernel Transport on Group Orbits: Oracle Inequalities and Minimax Rates
por: Nembe, Jocelyn
Publicado: (2025)

L^1 data fitting for Inverse Problems yields optimal rates of convergence in case of discretized white Gaussian noise
por: Bätz, Kristina, et al.
Publicado: (2025)

Automatic reproducing kernel and regularization for learning convolution kernels
por: Li, Haibo, et al.
Publicado: (2025)

Scalable iterative data-adaptive RKHS regularization
por: Li, Haibo, et al.
Publicado: (2024)

On a new notion of regularizer
por: Ramm, A. G.
Publicado: (2003)

Introduction to Regularization and Learning Methods for Inverse Problems
por: Bednarski, Danielle, et al.
Publicado: (2025)

On Learning the Optimal Regularization Parameter in Inverse Problems
por: Rodriguez, Jonathan Chirinos, et al.
Publicado: (2023)

Random tree Besov priors: Data-driven regularisation parameter selection
por: Kekkonen, Hanne, et al.
Publicado: (2026)

Kinematic formulas in convex geometry for non-compact groups
por: Anjos, Sílvia, et al.
Publicado: (2025)

Learning linear operators: Infinite-dimensional regression as a well-behaved non-compact inverse problem
por: Mollenhauer, Mattes, et al.
Publicado: (2022)

On identification in ill-posed linear regression
por: Finocchio, Gianluca, et al.
Publicado: (2025)

Some results on Krylov solvability in Banach space and connections to spectral theory
por: Caruso, Noe Angelo
Publicado: (2026)

On the Convergence of A Data-Driven Regularized Stochastic Gradient Descent for Nonlinear Ill-Posed Problems
por: Zhou, Zehui
Publicado: (2024)

Convergence rates of Landweber-type methods for inverse problems in Banach spaces
por: Jin, Qinian
Publicado: (2024)

Explainable Learning Based Regularization of Inverse Problems
por: Burger, Martin, et al.
Publicado: (2025)

A Scalable Sequential Framework for Dynamic Inverse Problems via Model Parameter Estimation
por: Keating, Aryeh, et al.
Publicado: (2026)

A simplified directional KeRF algorithm
por: Isidoros, Iakovidis, et al.
Publicado: (2024)

Spectral Equivariance and Geometric Transport in Reproducing Kernel Hilbert Spaces: A Unified Framework for Orthogonal Polynomial and Kernel Estimation
por: Nembé, Jocelyn
Publicado: (2025)

Representation Theorems for Convex Expectations and Semigroups on Path Space
por: Criens, David, et al.
Publicado: (2025)

Kernel-Based Nonparametric Tests For Shape Constraints
por: Sen, Rohan
Publicado: (2025)