Imagen de Portada

How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of Documents

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Lemberger, Pirmin, Saillenfest, Antoine
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning Statistics Theory 62-08 G.3
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Explaining Text Classifiers with Counterfactual Representations
por: Lemberger, Pirmin, et al.
Publicado: (2024)

Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints
por: Auddy, Arnab, et al.
Publicado: (2024)

CINDES: Classification induced neural density estimator and simulator
por: Dai, Dehao, et al.
Publicado: (2025)

Estimating a Function and Its Derivatives Under a Smoothness Condition
por: Lim, Eunji
Publicado: (2024)

Alignment-Sensitive Minimax Rates for Spectral Algorithms with Learned Kernels
por: Huang, Dongming, et al.
Publicado: (2025)

What Functions Does XGBoost Learn?
por: Ki, Dohyeong, et al.
Publicado: (2026)

MARS via LASSO
por: Ki, Dohyeong, et al.
Publicado: (2021)

Structure-Preserving Nonlinear Sufficient Dimension Reduction for Tensors
por: Lin, Dianjun, et al.
Publicado: (2025)

Factor Informed Double Deep Learning For Average Treatment Effect Estimation
por: Fan, Jianqing, et al.
Publicado: (2025)

Asymptotic confidence bands for centered purely random forests
por: Neumeyer, Natalie, et al.
Publicado: (2025)

Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
por: Gu, Yihong, et al.
Publicado: (2024)

On Regression in Extreme Regions
por: Clémençon, Stephan, et al.
Publicado: (2023)

Symmetry: a General Structure in Nonparametric Regression
por: Christie, Louis G., et al.
Publicado: (2024)

New Risk Bounds for 2D Total Variation Denoising
por: Chatterjee, Sabyasachi, et al.
Publicado: (2019)

Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers
por: Ching, Michelle, et al.
Publicado: (2026)

Optimal In-context Adaptivity and Distributional Robustness of Transformers
por: Ma, Tianyi, et al.
Publicado: (2025)

Functional Partial Least-Squares: Adaptive Estimation and Inference
por: Babii, Andrii, et al.
Publicado: (2024)

Inference with Mondrian Random Forests
por: Cattaneo, Matias D., et al.
Publicado: (2023)

Regularized least squares learning with heavy-tailed noise is minimax optimal
por: Mollenhauer, Mattes, et al.
Publicado: (2025)

A PAC-Bayes oracle inequality for sparse neural networks
por: Steffen, Maximilian F., et al.
Publicado: (2022)

Standardization of Weighted Ranking Correlation Coefficients
por: Lombardo, Pierangelo
Publicado: (2025)

Optimal minimax rate of learning nonlocal interaction kernels
por: Wang, Xiong, et al.
Publicado: (2023)

An Empirical Analysis of the Laplace and Neural Tangent Kernels
por: Lencevičius, Ronaldas Paulius
Publicado: (2022)

Understanding the Effect of GCN Convolutions in Regression Tasks
por: Chen, Juntong, et al.
Publicado: (2024)

On the Computational Efficiency of Bayesian Additive Regression Trees: An Asymptotic Analysis
por: Tan, Yan Shuo, et al.
Publicado: (2024)

On the Statistical Optimality of Optimal Decision Trees
por: Xu, Zineng, et al.
Publicado: (2026)

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

Statistical-Computational Trade-offs for Recursive Adaptive Partitioning Estimators
por: Tan, Yan Shuo, et al.
Publicado: (2024)

Noise-resilient penalty operators based on statistical differentiation schemes
por: Vidal, Marc, et al.
Publicado: (2026)

Deep learning with missing data
por: Ma, Tianyi, et al.
Publicado: (2025)

Nonlinear Concept Erasure: a Density Matching Approach
por: Saillenfest, Antoine, et al.
Publicado: (2025)

Estimating a regression function under possible heteroscedastic and heavy-tailed errors. Application to shape-restricted regression
por: Baraud, Yannick, et al.
Publicado: (2025)

Optimal Rate of Kernel Regression in Large Dimensions
por: Lu, Weihao, et al.
Publicado: (2023)

Are Ensembles Getting Better all the Time?
por: Mattei, Pierre-Alexandre, et al.
Publicado: (2023)

Asymptotic theory for extreme value generalized additive models
por: Yoshida, Takuma
Publicado: (2023)

On deviation probabilities in non-parametric regression with heavy-tailed noise
por: Ben-Hamou, Anna, et al.
Publicado: (2023)

Single-index models for extreme value index regression
por: Yoshida, Takuma
Publicado: (2022)

The Optimality of Kernel Classifiers in Sobolev Space
por: Lai, Jianfa, et al.
Publicado: (2024)

Adaptation using spatially distributed Gaussian Processes
por: Szabo, Botond, et al.
Publicado: (2023)

Automatic and location-adaptive estimation in functional single-index regression
por: Novo, Silvia, et al.
Publicado: (2024)