:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Averyanov, Yaroslav, Celisse, Alain
Format:	Preprint
Published:	2020
Subjects:	Machine Learning Statistics Theory
Online Access:	https://arxiv.org/abs/2007.06827
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Minimum discrepancy principle strategy for choosing $k$ in $k$-NN regression
by: Averyanov, Yaroslav, et al.
Published: (2020)

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

Representation learning with a transformer by contrastive learning for money laundering detection
by: Guéneau, Harold, et al.
Published: (2025)

Outrigger local polynomial regression
by: Young, Elliot H., et al.
Published: (2026)

Early-stopped aggregation: Adaptive inference with computational efficiency
by: Ohn, Ilsang, et al.
Published: (2026)

Improved learning theory for kernel distribution regression with two-stage sampling
by: Bachoc, François, et al.
Published: (2023)

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

A new approach to locally adaptive polynomial regression
by: Chatterjee, Sabyasachi, et al.
Published: (2024)

General reproducing properties in RKHS with application to derivative and integral operators
by: El-Boukkouri, Fatima-Zahrae, et al.
Published: (2025)

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

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

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)

Nonparametric logistic regression with deep learning
by: Yara, Atsutomo, 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)

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

Uncertainty quantification for iterative algorithms in linear models with application to early stopping
by: Bellec, Pierre C., et al.
Published: (2024)

Dynamic angular synchronization under smoothness constraints
by: Araya, Ernesto, et al.
Published: (2024)

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

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

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

Support estimation in high-dimensional heteroscedastic mean regression
by: Hermann, Philipp, et al.
Published: (2020)

Optimal score estimation via empirical Bayes smoothing
by: Wibisono, Andre, et al.
Published: (2024)

Adaptive sparse variational approximations for Gaussian process regression
by: Nieman, Dennis, et al.
Published: (2025)

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

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
by: Haas, Moritz, et al.
Published: (2023)

Consistent estimation of generative model representations in the data kernel perspective space
by: Acharyya, Aranyak, et al.
Published: (2024)

Eigen-convergence of Gaussian kernelized graph Laplacian by manifold heat interpolation
by: Cheng, Xiuyuan, et al.
Published: (2021)

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

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

Minimax rates of convergence for nonparametric regression under adversarial attacks
by: Peng, Jingfu, et al.
Published: (2024)

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

Canonical correlation regression with noisy data
by: Meza, Isaac, et al.
Published: (2025)

Kernel smoothing on manifolds
by: Bae, Eunseong, et al.
Published: (2026)

Minimax optimal differentially private synthetic data for smooth queries
by: Ding, Rundong, et al.
Published: (2026)

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

Joint estimation of smooth graph signals from partial linear measurements
by: Tyagi, Hemant
Published: (2025)

Parametric Mean-Field empirical Bayes in high-dimensional linear regression
by: Lee, Seunghyun, et al.
Published: (2026)

High-dimensional logistic regression with missing data: Imputation, regularization, and universality
by: Verchand, Kabir Aladin, et al.
Published: (2024)