Cover Image

Variance estimation in graphs with the fused lasso

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Padilla, Oscar Hernan Madrid
Format:	Preprint
Published:	2022
Subjects:	Statistics Theory Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dense ReLU Neural Networks for Temporal-spatial Model
by: Padilla, Carlos Misael Madrid, et al.
Published: (2024)

Optimal Bias-variance Tradeoff in Matrix and Tensor Estimation
by: Kumar, Shivam, et al.
Published: (2025)

Selective inference using randomized group lasso estimators for general models
by: Huang, Yiling, et al.
Published: (2023)

Network two-sample test for block models
by: Nguen, Chung Kyong, et al.
Published: (2024)

A causal fused lasso for interpretable heterogeneous treatment effects estimation
by: Padilla, Oscar Hernan Madrid, et al.
Published: (2021)

Variance Reduction for the Independent Metropolis Sampler
by: Liu, Siran, et al.
Published: (2024)

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

Variance-Aware Estimation of Kernel Mean Embedding
by: Wolfer, Geoffrey, et al.
Published: (2022)

On the Variance, Admissibility, and Stability of Empirical Risk Minimization
by: Kur, Gil, et al.
Published: (2023)

Precise Asymptotics and Refined Regret of Variance-Aware UCB
by: Fan, Yingying, et al.
Published: (2024)

The Benefits of Balance: From Information Projections to Variance Reduction
by: Liu, Lang, et al.
Published: (2024)

Random Forests as Statistical Procedures: Design, Variance, and Dependence
by: O'Connell, Nathaniel S.
Published: (2026)

Jackknife Variance Estimation for Hájek-Dominated Generalized U-Statistics
by: Juergens, Jakob R.
Published: (2025)

Fixed-Confidence Best Arm Identification with Decreasing Variance
by: Roychowdhury, Tamojeet, et al.
Published: (2025)

The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
by: Arratia, Argimiro, et al.
Published: (2025)

Variance-based sensitivity analysis in the presence of correlated input variables
by: Most, Thomas
Published: (2024)

Markov Chain Variance Estimation: A Stochastic Approximation Approach
by: Agrawal, Shubhada, et al.
Published: (2024)

A Random Matrix Perspective of Echo State Networks: From Precise Bias--Variance Characterization to Optimal Regularization
by: Moakher, Yessin, et al.
Published: (2025)

Sharp Concentration Inequalities: Phase Transition and Mixing of Orlicz Tails with Variance
by: Shen, Yinan, et al.
Published: (2026)

Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction
by: Byambadalai, Undral, et al.
Published: (2024)

On consistent estimation of dimension values
by: Cholaquidis, Alejandro, et al.
Published: (2024)

Optimal community detection in dense bipartite graphs
by: Chhor, Julien, et al.
Published: (2025)

Sample complexity of Schrödinger potential estimation
by: Puchkin, Nikita, et al.
Published: (2025)

Minimax Optimal Variance-Aware Regret Bounds for Multinomial Logistic MDPs
by: Boudart, Pierre, et al.
Published: (2026)

Statistical-computational gap in multiple Gaussian graph alignment
by: Even, Bertrand, et al.
Published: (2025)

Precise Asymptotics of Bagging Regularized M-estimators
by: Koriyama, Takuya, et al.
Published: (2024)

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

Proper scoring rules for estimation and forecast evaluation
by: Waghmare, Kartik, et al.
Published: (2025)

Learning manifold diffusion semigroups from graph transition matrices
by: Cheng, Xiuyuan, et al.
Published: (2026)

Distribution free M-estimation
by: Areces, Felipe, et al.
Published: (2025)

A review of predictive uncertainty estimation with machine learning
by: Tyralis, Hristos, et al.
Published: (2022)

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

Robust estimation with Lasso when outputs are adversarially contaminated
by: Sasai, Takeyuki, et al.
Published: (2020)

Rates of convergence for density estimation with generative adversarial networks
by: Puchkin, Nikita, et al.
Published: (2021)

Performance of the empirical median for location estimation in heteroscedastic settings
by: Louati, Sirine
Published: (2025)

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

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

Observable adjustments in single-index models for regularized M-estimators
by: Bellec, Pierre C
Published: (2022)

Robust density estimation over star-shaped density classes
by: Liu, Xiaolong, et al.
Published: (2025)

Distributed quasi-Newton robust estimation under differential privacy
by: Wang, Chuhan, et al.
Published: (2024)