Reproducing Kernel Hilbert Spaces

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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MiNCE: Nonparametric, Strongly Consistent Confidence Envelopes for Band-Limited Functions and their Smoothed Spectra
Sep 8, 2026Balázs Csanád Csáji, Bálint HorváthConfidence Intervals Exponential Family
HUN-REN SZTAKI: Institute for Computer Science and Control, Budapest, Hungary · Institute of Mathematics, Eötvös Loránd University (ELTE), Budapest, Hungary
Revisiting Thinning Methods for Kernel Learning Problems
Sep 7, 2026Blanca Cano-Camarero, Yago R. Aguado-Carrillo-de-Albornoz, Ángela Fernández-Pascual +1Kernel Method Reproducing Kernel Hilbert Spaces
Departamento de Ingeniería Informática, Universidad Autónoma de Madrid, Madrid, Spain
Simple-regret rates and minimax optimality of fixed-prior expected improvement in Matérn and squared-exponential RKHSs
Jul 31, 2026Emmanuel Vazquez, Sébastien PetitReproducing Kernel Hilbert Spaces Minimax Rate
Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, 91190 Gif-sur-Yvette, France · Laboratoire national de métrologie et d’essais (LNE), 1 rue Gaston Boissier, 75724 Paris Cedex 15, France
PIKS: Universal Physics-Informed Kernel Methods
Jul 29, 2026Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1Physics-Informed Learning Reproducing Kernel Hilbert Spaces
MaLGa Center, DIBRIS, Università degli Studi di Genova, Genoa, Italy · MaLGa Center, DIMA, Università degli Studi di Genova, Genoa, Italy · Istituto Italiano di Tecnologia, Genoa, Italy
Subsampling for supervised learning in reproducing kernel Hilbert spaces
Jun 19, 2026Eyal Vayness, Maxime SangnierReproducing Kernel Hilbert Spaces Kernel Hilbert Spaces
Sorbonne Universit´e, LPSM, Paris, France
Maximum Entropy Inverse Reinforcement Learning for Mean-Field Games with Average Reward
Jun 15, 2026Şevket Kaan Alkır, Naci Saldı, Berkay Anahtarcı +1Mean Field Games Entropy Regularized Reinforcement Learning
Department of Mathematics Bilkent University 06800 Ankara, Turkey · Department of Mathematical Engineering Özyeğin University 34794 Istanbul, Turkey
Structuring agentic AI for HPC code modernization
Jun 7, 2026Anthony Marinov, Igor SfiligoiHigh-Performance Computing Cloud
San Diego Supercomputer Center University of California, San Diego La Jolla, California, United States
FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes
May 28, 2026Donglai Xiang, Vismay Modi, Rishit Dagli +5Finite Element Method Reduced-Order Models
NVIDIA · 1NVIDIA · University of Toronto
Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels
May 17, 2026Binyamin Perets, Shie MannorFalse Discovery Rate Reproducing Kernel Hilbert Spaces
Technion – Israel Institute of Technology · NVIDIA
Wahkon: A Statistically Principled Deep RKHS Superposition Network
May 13, 2026Yongkai Chen, Wenxuan Zhong, Ping MaReproducing Kernel Hilbert Spaces Kolmogorov-Arnold Networks
Department of Statistics, Harvard University · Department of Statistics, University of Georgia
DriftXpress: Faster Drifting Models via Projected RKHS Fields
May 12, 2026Ali Falahati, Elliot Creager, Gautam Kamath +1Diffusion Models Generative Models
Cheriton School of Computer Science, University of Waterloo and Vector Institute. · Department of Electrical and Computer Engineering, University of Waterloo and Vector Institute. · A*STAR Centre for Frontier AI Research, Singapore.
Nearly-Optimal Algorithm for Adversarial Kernelized Bandits
May 11, 2026Shogo IwazakiBandits Bounded Adversary
LY Corporation Tokyo, Japan
Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework
May 10, 2026Wenbin Wang, Colin N. JonesBayesian Optimization Reproducing Kernel Hilbert Spaces
Automatic Control Laboratory, EPFL
The Pokémon Theorem and other Fairness Impossibility Results
May 9, 2026Daniel Matsui Smola, Alex SmolaAlgorithmic Fairness Impossibility
Department of Computer Science University of Washington Seattle, WA · Boson AI Santa Clara, CA
A Behavioral Framework for Data-Driven Modeling of Nonlinear Systems in Vector-Valued Reproducing Kernel Hilbert Spaces
May 8, 2026Boya Hou, Maxim RaginskySystem Identification Reproducing Kernel Hilbert Spaces
Carl R. Woese Institute for Genomic Biology and the Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA · Department of Electrical and Computer Engineering and the Coordinated Science Laboratory, University of Illinois Urbana-Champaign, Urbana, IL, 61801 USA
Convex-Geometric Error Bounds for Positive-Weight Kernel Quadrature
May 7, 2026Satoshi HayakawaReproducing Kernel Hilbert Spaces Bayesian Quadrature
The University of Tokyo
A Robust Unsupervised Domain Adaptation Framework for Medical Image Classification Using RKHS-MMD
May 5, 2026Sapna Sachan, Rakesh Kumar Sanodiya, Amulya Kumar MahtoMedical Image Classification Domain Adaptation
Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Guwahati, India · Indian Institute of Information Technology, Design and Manufacturing,2026 Jabalpur, India
A Universal Reproducing Kernel Hilbert Space from Polynomial Alignment and IMQ Distance
May 5, 2026Taha BouhsineReproducing Kernel Hilbert Spaces Fourier
Azetta AI
Dynamic Regret for Online Regression in RKHS via Discounted VAW and Subspace Approximation
Apr 27, 2026Dmitry B. Rokhlin, Georgiy A. KarapetyantsReproducing Kernel Hilbert Spaces Kernel Method
Institute of Mathematics, Mechanics and Computer Sciences of the Southern Federal University
Generalising maximum mean discrepancy: kernelised functional Bregman divergences
Apr 27, 2026Russell Tsuchida, Frank NielsenBregman Divergences Reproducing Kernel Hilbert Spaces
Department of Data Science and AI, Monash University, Australia. · Sony Computer Science Laboratories, Inc. Tokyo, Japan.
Explicit integral representations and quantitative bounds for two-layer ReLU networks
Apr 25, 2026Anthony LeeRectified Linear Unit Networks Rectified Linear Unit
School of Mathematics, University of Bristol
Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces
Apr 24, 2026Natanael Alpay, Emeric BattagliaReproducing Kernel Hilbert Spaces Stochastic Gradient Descent
Structural interpretability in SVMs with truncated orthogonal polynomial kernels
Apr 16, 2026Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. HuertasSupport Vector Machines Kernel Method
Dpto. de Biociencias, Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Madrid, C/ Tajo, s/n, 28670 Villaviciosa de Odón, Madrid, Spain. · Dpto. de Matemáticas, Facultad de Ciencias, Universidad Autónoma de Madrid, C/ Francisco Tomás y Valiente, 7, 28049 Madrid, Spain. · Instituto de Ciencias Matemáticas (ICMAT), Campus de Cantoblanco UAM, C/ Nicolás Cabrera, 13–15, 28049 Madrid, Spain. +1
Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
Mar 17, 2026Amon Lahr, Anna Scampicchio, Johannes Köhler +1Gaussian Process Reproducing Kernel Hilbert Spaces
Institute for Dynamical Systems and Control, ETH Zurich, Zurich, Switzerland
A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
Sep 14, 2025Jia-Qi Yang, Lei ShiProbabilistic Operator Learning Nonlinear Operators
School of Mathematical Sciences and Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University, Shanghai 200433, China.
Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences
Jun 20, 2025Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1Reproducing Kernel Hilbert Spaces Kernel Hilbert Spaces
EURECOM · University of Tübingen, Tübingen AI Center Max Planck Institute for Intelligent Systems · Adelaide University Australian Institute for Machine Learning +1
Which Spaces can be Embedded in $L_p$ -type Reproducing Kernel Banach Space? A Characterization via Metric Entropy
Oct 14, 2024Yiping Lu, Daozhe Lin, Qiang DuReproducing Kernel Hilbert Spaces Learnability
Industrial Engineering & Management Sciences, Northwestern University · Department of Applied Physics & Applied Mathematics, Columbia University
DynaWeightPnP: Toward global real-time 3D-2D solver in PnP without correspondences
Sep 27, 2024Jingwei Song, Maani GhaffariImage Registration Reproducing Kernel Hilbert Spaces
United Imaging Research Institute of Intelligent Imaging, Beijing 100144, China, Beijing, 100144, China · University of Michigan, Ann Arbor, 48109, MI, USA
Nonparametric Control Koopman Operators
May 12, 2024Petar Bevanda, Bas Driessen, Lucian Cristian Iacob +3Koopman Operator Reproducing Kernel Hilbert Spaces
Chair of Information-oriented Control, TU München, Germany · Control Systems Group, TU/e, Eindhoven, The Netherlands · Systems and Control Laboratory, HUNREN Institute for Computer Science and Control, Budapest, Hungary +2

Sep 8, 2026, math.STJ/K move · Enter open · S save

MiNCE: Nonparametric, Strongly Consistent Confidence Envelopes for Band-Limited Functions and their Smoothed Spectra

Balázs Csanád Csáji, Bálint Horváth

HUN-REN SZTAKI: Institute for Computer Science and Control, Budapest, Hungary · Institute of Mathematics, Eötvös Loránd University (ELTE), Budapest, Hungary

Confidence Intervals Exponential Family Reproducing Kernel Hilbert Spaces Spectral Filtering

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Minimum-norm confidence envelope strategies offer a nonparametric approach to constructing nonasymptotic, simultaneous confidence regions for band-limited functions, exploiting the theory of Reproducing Kernel Hilbert Spaces (RKHS). While the finite-sample coverage guarantees of these envelopes have been established, their consistency has not been analyzed so far. In this paper, we study this construction, here termed the Minimum-Norm Confidence Envelope (MiNCE) framework, and establish the strong uniform consistency of the resulting bands, both for noise-free and noisy observation models, under mild assumptions on the measurement noises. We further extend this formulation to the frequency domain, deriving nonasymptotic, simultaneous, strongly uniformly consistent confidence bands for the smoothed spectra. Numerical experiments in nonparametric regression and spectral estimation empirically confirm our theoretical results, illustrating the contraction of the confidence envelopes toward the target function as the sample size increases.

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