Saved in:
| Main Authors: | Kengne, William, Wade, Modou |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.11138 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Robust deep learning from weakly dependent data
by: Kengne, William, et al.
Published: (2024)
by: Kengne, William, et al.
Published: (2024)
A general framework for deep learning
by: Kengne, William, et al.
Published: (2025)
by: Kengne, William, et al.
Published: (2025)
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality
by: Kengne, William, et al.
Published: (2024)
by: Kengne, William, et al.
Published: (2024)
Minimum discrepancy principle strategy for choosing $k$ in $k$-NN regression
by: Averyanov, Yaroslav, et al.
Published: (2020)
by: Averyanov, Yaroslav, et al.
Published: (2020)
Nonparametric logistic regression with deep learning
by: Yara, Atsutomo, et al.
Published: (2024)
by: Yara, Atsutomo, et al.
Published: (2024)
Strong identifiability and parameter learning in regression with heterogeneous response
by: Do, Dat, et al.
Published: (2022)
by: Do, Dat, et al.
Published: (2022)
Minimax optimal transfer learning for high-dimensional additive regression
by: Moon, Seung Hyun
Published: (2025)
by: Moon, Seung Hyun
Published: (2025)
Tessellation Localized Transfer learning for nonparametric regression
by: Halconruy, Hélène, et al.
Published: (2026)
by: Halconruy, Hélène, et al.
Published: (2026)
Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
by: Noskov, Fedor, et al.
Published: (2025)
by: Noskov, Fedor, et al.
Published: (2025)
Adaptive posterior concentration rates for sparse high-dimensional linear regression with random design and unknown error variance
by: Mai, The Tien
Published: (2024)
by: Mai, The Tien
Published: (2024)
Improved learning theory for kernel distribution regression with two-stage sampling
by: Bachoc, François, et al.
Published: (2023)
by: Bachoc, François, et al.
Published: (2023)
Augmented transfer regression learning for completely missing covariates
by: Zhao, Huali, et al.
Published: (2026)
by: Zhao, Huali, et al.
Published: (2026)
Bayes optimal learning in high-dimensional linear regression with network side information
by: Nandy, Sagnik, et al.
Published: (2023)
by: Nandy, Sagnik, et al.
Published: (2023)
Adversarial learning for nonparametric regression: Minimax rate and adaptive estimation
by: Peng, Jingfu, et al.
Published: (2025)
by: Peng, Jingfu, et al.
Published: (2025)
Generalization error of min-norm interpolators in transfer learning
by: Song, Yanke, et al.
Published: (2024)
by: Song, Yanke, et al.
Published: (2024)
Identifiability of the minimum-trace directed acyclic graph and hill climbing algorithms without strict local optima under weakly increasing error variances
by: Chang, Hyunwoong, et al.
Published: (2025)
by: Chang, Hyunwoong, et al.
Published: (2025)
Statistical Agnostic Regression: a machine learning method to validate regression models
by: Gorriz, Juan M, et al.
Published: (2024)
by: Gorriz, Juan M, et al.
Published: (2024)
Spike-timing-dependent Hebbian learning as noisy gradient descent
by: Dexheimer, Niklas, et al.
Published: (2025)
by: Dexheimer, Niklas, et al.
Published: (2025)
Predicting path-dependent processes by deep learning
by: Zheng, Xudong, et al.
Published: (2024)
by: Zheng, Xudong, et al.
Published: (2024)
Metric space valued Fréchet regression
by: Györfi, László, et al.
Published: (2026)
by: Györfi, László, et al.
Published: (2026)
On damage of interpolation to adversarial robustness in regression
by: Peng, Jingfu, et al.
Published: (2026)
by: Peng, Jingfu, et al.
Published: (2026)
Variational Bayesian Bow tie Neural Networks with Shrinkage
by: Sheinkman, Alisa, et al.
Published: (2024)
by: Sheinkman, Alisa, et al.
Published: (2024)
Dynamical local Fréchet curve regression in manifolds
by: Ruiz-Medina, M. D., et al.
Published: (2025)
by: Ruiz-Medina, M. D., et al.
Published: (2025)
Covariate shift in nonparametric regression with Markovian design
by: Trottner, Lukas
Published: (2023)
by: Trottner, Lukas
Published: (2023)
Outrigger local polynomial regression
by: Young, Elliot H., et al.
Published: (2026)
by: Young, Elliot H., et al.
Published: (2026)
Adaptive sparse variational approximations for Gaussian process regression
by: Nieman, Dennis, et al.
Published: (2025)
by: Nieman, Dennis, et al.
Published: (2025)
Support estimation in high-dimensional heteroscedastic mean regression
by: Hermann, Philipp, et al.
Published: (2020)
by: Hermann, Philipp, et al.
Published: (2020)
Early stopping and polynomial smoothing in regression with reproducing kernels
by: Averyanov, Yaroslav, et al.
Published: (2020)
by: Averyanov, Yaroslav, et al.
Published: (2020)
Finite-sample performance of the maximum likelihood estimator in logistic regression
by: Chardon, Hugo, et al.
Published: (2024)
by: Chardon, Hugo, et al.
Published: (2024)
Minimax rates of convergence for nonparametric regression under adversarial attacks
by: Peng, Jingfu, et al.
Published: (2024)
by: Peng, Jingfu, et al.
Published: (2024)
Canonical correlation regression with noisy data
by: Meza, Isaac, et al.
Published: (2025)
by: Meza, Isaac, et al.
Published: (2025)
The generalized underlap coefficient with an application in clustering
by: Zhang, Zhaoxi, et al.
Published: (2026)
by: Zhang, Zhaoxi, et al.
Published: (2026)
On the sample complexity of parameter estimation in logistic regression with normal design
by: Hsu, Daniel, et al.
Published: (2023)
by: Hsu, Daniel, et al.
Published: (2023)
Parametric Mean-Field empirical Bayes in high-dimensional linear regression
by: Lee, Seunghyun, et al.
Published: (2026)
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)
by: Verchand, Kabir Aladin, et al.
Published: (2024)
Asymptotic breakdown point analysis of the minimum density power divergence estimator under independent non-homogeneous setups
by: Jana, Suryasis, et al.
Published: (2025)
by: Jana, Suryasis, et al.
Published: (2025)
Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
by: Stankewitz, Bernhard, et al.
Published: (2024)
by: Stankewitz, Bernhard, et al.
Published: (2024)
Nonparametric regression using over-parameterized shallow ReLU neural networks
by: Yang, Yunfei, et al.
Published: (2023)
by: Yang, Yunfei, et al.
Published: (2023)
Theoretical limits of descending $\ell_0$ sparse-regression ML algorithms
by: Stojnic, Mihailo
Published: (2024)
by: Stojnic, Mihailo
Published: (2024)
Ridge interpolators in correlated factor regression models -- exact risk analysis
by: Stojnic, Mihailo
Published: (2024)
by: Stojnic, Mihailo
Published: (2024)
Similar Items
-
Robust deep learning from weakly dependent data
by: Kengne, William, et al.
Published: (2024) -
A general framework for deep learning
by: Kengne, William, et al.
Published: (2025) -
Deep learning from strongly mixing observations: Sparse-penalized regularization and minimax optimality
by: Kengne, William, et al.
Published: (2024) -
Minimum discrepancy principle strategy for choosing $k$ in $k$-NN regression
by: Averyanov, Yaroslav, et al.
Published: (2020) -
Nonparametric logistic regression with deep learning
by: Yara, Atsutomo, et al.
Published: (2024)