Imagen de Portada

General reproducing properties in RKHS with application to derivative and integral operators

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	El-Boukkouri, Fatima-Zahrae, Garnier, Josselin, Roustant, Olivier
Formato:	Preprint
Publicado:	2025
Materias:	Statistics Theory Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Multifidelity Gaussian process regression for solving nonlinear partial differential equations
por: El-Boukkouri, Fatima-Zahrae, et al.
Publicado: (2026)

Non-asymptotic confidence regions on RKHS. The Paley-Wiener and standard Sobolev space cases
por: Gamboa, Fabrice, et al.
Publicado: (2025)

Approximation of RKHS Functionals by Neural Networks
por: Zhou, Tian-Yi, et al.
Publicado: (2024)

Approximate full conformal prediction in an RKHS
por: Razafindrakoto, Davidson Lova, et al.
Publicado: (2026)

Gradient-enhanced global sensitivity analysis with Poincar{é} chaos expansions
por: Roustant, O, et al.
Publicado: (2025)

Max-sliced Wasserstein concentration and uniform ratio bounds of empirical measures on RKHS
por: Han, Ruiyu, et al.
Publicado: (2024)

Early stopping and polynomial smoothing in regression with reproducing kernels
por: Averyanov, Yaroslav, et al.
Publicado: (2020)

Adaptive learning of density ratios in RKHS
por: Zellinger, Werner, et al.
Publicado: (2023)

On one dimensional weighted Poincare inequalities for Global Sensitivity Analysis
por: Heredia, David, et al.
Publicado: (2024)

On Goodhart's law, with an application to value alignment
por: El-Mhamdi, El-Mahdi, et al.
Publicado: (2024)

Fast pick-freeze estimation of Sobol' sensitivity maps using basis expansions
por: Sao, Yuri, et al.
Publicado: (2024)

A Pure Hypothesis Test for Inhomogeneous Random Graph Models Based on a Kernelised Stein Discrepancy
por: Fatima, Anum, et al.
Publicado: (2025)

The Strong, Weak and Benign Goodhart's law. An independence-free and paradigm-agnostic formalisation
por: Majka, Adrien, et al.
Publicado: (2025)

Meta-Learning and representation learner: A short theoretical note
por: Bouchattaoui, Mouad El
Publicado: (2024)

A reproducible comparative study of categorical kernels for Gaussian process regression, with new clustering-based nested kernels
por: Perez, Raphaël Carpintero, et al.
Publicado: (2025)

Minimax Adaptive Online Nonparametric Regression over Besov Spaces
por: Liautaud, Paul, et al.
Publicado: (2025)

Minimax-optimal and Locally-adaptive Online Nonparametric Regression
por: Liautaud, Paul, et al.
Publicado: (2024)

High-Probability Minimax Adaptive Estimation in Besov Spaces via Online-to-Batch
por: Liautaud, Paul, et al.
Publicado: (2026)

A Full Adagrad algorithm with O(Nd) operations
por: Godichon-Baggioni, Antoine, et al.
Publicado: (2024)

Convergence of Statistical Estimators via Mutual Information Bounds
por: Khribch, El Mahdi, et al.
Publicado: (2024)

Adaptive variational Bayes: Optimality, computation and applications
por: Ohn, Ilsang, et al.
Publicado: (2021)

Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression
por: Hanchi, Ayoub El, et al.
Publicado: (2023)

A unified recipe for deriving (time-uniform) PAC-Bayes bounds
por: Chugg, Ben, et al.
Publicado: (2023)

Stochastic Optimization in Semi-Discrete Optimal Transport: Convergence Analysis and Minimax Rate
por: Genans, Ferdinand, et al.
Publicado: (2025)

A Geometric Analysis of PCA
por: Hanchi, Ayoub El, et al.
Publicado: (2025)

The loss landscape of deep linear neural networks: a second-order analysis
por: Achour, El Mehdi, et al.
Publicado: (2021)

Effective regions and kernels in continuous sparse regularisation, with application to sketched mixtures
por: De Castro, Yohann, et al.
Publicado: (2025)

Derivatives and residual distribution of regularized M-estimators with application to adaptive tuning
por: Bellec, Pierre C, et al.
Publicado: (2021)

On Monotonicity in AI Alignment
por: Bareilles, Gilles, et al.
Publicado: (2025)

On two ways to use determinantal point processes for Monte Carlo integration
por: Gautier, Guillaume, et al.
Publicado: (2026)

Testing properties of trees in graphical models with covariance queries
por: Burova, Sofiya, et al.
Publicado: (2026)

Pseudo-Observations and Super Learner for the Estimation of the Restricted Mean Survival Time
por: Cwiling, Ariane, et al.
Publicado: (2024)

Joint stochastic localization and applications
por: Alberts, Tom, et al.
Publicado: (2025)

Estimation and model errors in Gaussian-process-based Sensitivity Analysis of functional outputs
por: Sáo, Yuri Taglieri, et al.
Publicado: (2025)

Minimax Linear Regression under the Quantile Risk
por: Hanchi, Ayoub El, et al.
Publicado: (2024)

On the Efficiency of ERM in Feature Learning
por: Hanchi, Ayoub El, et al.
Publicado: (2024)

A theoretical framework for M-posteriors: frequentist guarantees and robustness properties
por: Marusic, Juraj, et al.
Publicado: (2025)

The generalized underlap coefficient with an application in clustering
por: Zhang, Zhaoxi, et al.
Publicado: (2026)

Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery
por: Saux, Patrick
Publicado: (2024)

Information-Geometric Decomposition of Generalization Error in Unsupervised Learning
por: Kim, Gilhan
Publicado: (2026)