:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Chen, Likai, Keilbar, Georg, Wu, Wei Biao
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Statistics Theory
Acceso en línea:	https://arxiv.org/abs/2505.13299
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Sharp Gaussian approximations for Decentralized Federated Learning
por: Bonnerjee, Soham, et al.
Publicado: (2025)

On the best approximation by finite Gaussian mixtures
por: Ma, Yun, et al.
Publicado: (2024)

Gaussian-Smoothed Sliced Probability Divergences
por: Alaya, Mokhtar Z., et al.
Publicado: (2024)

Factor Augmented High-Dimensional SGD
por: Li, Shubo, et al.
Publicado: (2026)

Convex SGD: Generalization Without Early Stopping
por: Hendrickx, Julien, et al.
Publicado: (2024)

Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping
por: Şimşekli, Umut, et al.
Publicado: (2024)

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

Inverting estimating equations for causal inference on quantiles
por: Cheng, Chao, et al.
Publicado: (2024)

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

A unified construction for series representations and finite approximations of completely random measures
por: Lee, Juho, et al.
Publicado: (2019)

A sparse PAC-Bayesian approach for high-dimensional quantile prediction
por: Mai, The Tien
Publicado: (2024)

Estimation of High-dimensional Nonlinear Vector Autoregressive Models
por: Han, Yuefeng, et al.
Publicado: (2025)

Contraction rates for conjugate gradient and Lanczos approximate posteriors in Gaussian process regression
por: Stankewitz, Bernhard, et al.
Publicado: (2024)

SGD with Dependent Data: Optimal Estimation, Regret, and Inference
por: Shen, Yinan, et al.
Publicado: (2026)

Shapley Curves: A Smoothing Perspective
por: Miftachov, Ratmir, et al.
Publicado: (2022)

Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression
por: Tan, Kai, et al.
Publicado: (2024)

High-dimensional Limit of SGD for Diagonal Linear Networks
por: Malaxechebarría, Begoña García, et al.
Publicado: (2026)

Statistical Inference for Linear Functionals of Online Least-squares SGD when $t \gtrsim d^{1+δ}$
por: Agrawalla, Bhavya, et al.
Publicado: (2025)

Jointly Modeling and Clustering Tensors in High Dimensions
por: Cai, Biao, et al.
Publicado: (2021)

AdaDPIGU: Differentially Private SGD with Adaptive Clipping and Importance-Based Gradient Updates for Deep Neural Networks
por: Zhang, Huiqi, et al.
Publicado: (2025)

Online Covariance Estimation in Averaged SGD: Improved Batch-Mean Rates and Minimax Optimality via Trajectory Regression
por: Ni, Yijin, et al.
Publicado: (2026)

Linear cost and exponentially convergent approximation of Gaussian Matérn processes on intervals
por: Bolin, David, et al.
Publicado: (2024)

Gaussian random field approximation via Stein's method with applications to wide random neural networks
por: Balasubramanian, Krishnakumar, et al.
Publicado: (2023)

Efficient Agnostic Learning with Average Smoothness
por: Hanneke, Steve, et al.
Publicado: (2023)

Adaptive Smooth Non-Stationary Bandits
por: Suk, Joe
Publicado: (2024)

A Piecewise Lyapunov Analysis of Sub-quadratic SGD: Applications to Robust and Quantile Regression
por: Zhang, Yixuan, et al.
Publicado: (2025)

Plugin Estimation of Smooth Optimal Transport Maps
por: Manole, Tudor, et al.
Publicado: (2021)

Improved Guarantees for Langevin Monte Carlo with Average Smoothness
por: Dalalyan, Arnak S., et al.
Publicado: (2026)

High-probability sample complexities for policy evaluation with linear function approximation
por: Li, Gen, et al.
Publicado: (2023)

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

Gaussian and Non-Gaussian Universality of Data Augmentation
por: Huang, Kevin Han, et al.
Publicado: (2022)

Local Minima Structures in Gaussian Mixture Models
por: Chen, Yudong, et al.
Publicado: (2020)

Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression
por: Agrawalla, Bhavya, et al.
Publicado: (2023)

Constructive Approximation under Carleman's Condition, with Applications to Smoothed Analysis
por: Koehler, Frederic, et al.
Publicado: (2025)

Dimension lower bounds for linear approaches to function approximation
por: Hsu, Daniel
Publicado: (2025)

High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance
por: Balasubramanian, Krishnakumar, et al.
Publicado: (2023)

$L_2$-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error
por: Fujisawa, Masahiro, et al.
Publicado: (2025)

Benign overfitting in Fixed Dimension via Physics-Informed Learning with Smooth Inductive Bias
por: Wong, Honam, et al.
Publicado: (2024)

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

Sliced gradient-enhanced Kriging for high-dimensional function approximation
por: Cheng, Kai, et al.
Publicado: (2022)