Cover Image

Improved learning theory for kernel distribution regression with two-stage sampling

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bachoc, François, Béthune, Louis, González-Sanz, Alberto, Loubes, Jean-Michel
Format:	Preprint
Published:	2023
Subjects:	Statistics Theory Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Wasserstein Spatial Depth
by: Bachoc, François, et al.
Published: (2024)

PLS-based approach for fair representation learning
by: De-Diego, Elena M., et al.
Published: (2025)

Learning with Differentially Private (Sliced) Wasserstein Gradients
by: Rodríguez-Vítores, David, et al.
Published: (2025)

Large-Sample Properties of Non-Stationary Source Separation for Gaussian Signals
by: Bachoc, François, et al.
Published: (2022)

Weak limits of entropy regularized Optimal Transport; potentials, plans and divergences
by: Gonzalez-Sanz, Alberto, et al.
Published: (2022)

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

Physics-informed kernel learning
by: Doumèche, Nathan, et al.
Published: (2024)

Hoeffding decomposition of black-box models with dependent inputs
by: Idrissi, Marouane Il, et al.
Published: (2023)

Geometry-induced Regularization in Deep ReLU Neural Networks
by: Bona-Pellissier, Joachim, et al.
Published: (2024)

Nonparametric logistic regression with deep learning
by: Yara, Atsutomo, et al.
Published: (2024)

Average Gradient Outer Product in kernel regression provably recovers the central subspace for multi-index models
by: Zhu, Libin, et al.
Published: (2026)

Scalable and adaptive prediction bands with kernel sum-of-squares
by: Allain, Louis, et al.
Published: (2025)

When majority rules, minority loses: bias amplification of gradient descent
by: Bachoc, François, et al.
Published: (2025)

Strong identifiability and parameter learning in regression with heterogeneous response
by: Do, Dat, et al.
Published: (2022)

Minimax optimal transfer learning for high-dimensional additive regression
by: Moon, Seung Hyun
Published: (2025)

A non-asymptotic distributional theory of approximate message passing for sparse and robust regression
by: Li, Gen, et al.
Published: (2024)

Tessellation Localized Transfer learning for nonparametric regression
by: Halconruy, Hélène, et al.
Published: (2026)

Bayes optimal learning in high-dimensional linear regression with network side information
by: Nandy, Sagnik, et al.
Published: (2023)

Deep regression learning from dependent observations with minimum error entropy principle
by: Kengne, William, et al.
Published: (2026)

Augmented transfer regression learning for completely missing covariates
by: Zhao, Huali, et al.
Published: (2026)

Closed-form $\ell_r$ norm scaling with data for overparameterized linear regression and diagonal linear networks under $\ell_p$ bias
by: Zhang, Shuofeng, et al.
Published: (2025)

Scale estimation and rate-unbiasedness for Gaussian processes under smoothness misspecification
by: Karvonen, Toni, et al.
Published: (2021)

Adversarial learning for nonparametric regression: Minimax rate and adaptive estimation
by: Peng, Jingfu, et al.
Published: (2025)

Off-the-grid learning of mixtures from a continuous dictionary
by: Butucea, Cristina, et al.
Published: (2022)

Simultaneous off-the-grid learning of mixtures issued from a continuous dictionary
by: Butucea, Cristina, et al.
Published: (2022)

High dimensional theory of two-phase optimizers
by: Agarwala, Atish
Published: (2026)

Finite-sample performance of the maximum likelihood estimator in logistic regression
by: Chardon, Hugo, et al.
Published: (2024)

Sharp asymptotic theory for Q-learning with LDTZ learning rate and its generalization
by: Bonnerjee, Soham, et al.
Published: (2026)

Iteratively reweighted kernel machines efficiently learn sparse functions
by: Zhu, Libin, et al.
Published: (2025)

Statistical Agnostic Regression: a machine learning method to validate regression models
by: Gorriz, Juan M, et al.
Published: (2024)

Statistical learning theory and Occam's razor: The core argument
by: Sterkenburg, Tom F.
Published: (2023)

On the sample complexity of parameter estimation in logistic regression with normal design
by: Hsu, Daniel, et al.
Published: (2023)

The phase diagram of kernel interpolation in large dimensions
by: Zhang, Haobo, et al.
Published: (2024)

On the number of modes of Gaussian kernel density estimators
by: Geshkovski, Borjan, et al.
Published: (2024)

Metric space valued Fréchet regression
by: Györfi, László, et al.
Published: (2026)

On damage of interpolation to adversarial robustness in regression
by: Peng, Jingfu, et al.
Published: (2026)

Covariate shift in nonparametric regression with Markovian design
by: Trottner, Lukas
Published: (2023)

Dynamical local Fréchet curve regression in manifolds
by: Ruiz-Medina, M. D., et al.
Published: (2025)

On the Pinsker bound of inner product kernel regression in large dimensions
by: Lu, Weihao, et al.
Published: (2024)

Gradient dynamics for low-rank fine-tuning beyond kernels
by: Dayi, Arif Kerem, et al.
Published: (2024)