:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Bachoc, François, Béthune, Louis, González-Sanz, Alberto, Loubes, Jean-Michel
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Statistics Theory Machine Learning
Accesso online:	https://arxiv.org/abs/2308.14335
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Wasserstein Spatial Depth
di: Bachoc, François, et al.
Pubblicazione: (2024)

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

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

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

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

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

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

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

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

Physics-informed kernel learning
di: Doumèche, Nathan, et al.
Pubblicazione: (2024)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Distributional Limit Theory for Optimal Transport
di: del Barrio, Eustasio, et al.
Pubblicazione: (2025)

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

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

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

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

High dimensional theory of two-phase optimizers
di: Agarwala, Atish
Pubblicazione: (2026)

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

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

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

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

Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations
di: Ferrere, Baptiste, et al.
Pubblicazione: (2026)

Inference post region selection
di: Bontemps, Dominique, et al.
Pubblicazione: (2025)

Contraction rates and projection subspace estimation with Gaussian process priors in high dimension
di: Odin, Elie, et al.
Pubblicazione: (2024)

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

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

Fréchet regression of multivariate distributions with nonparanormal transport
di: Park, Junyoung, et al.
Pubblicazione: (2026)