Cover Image

Fast approximative estimation of conditional Shapley values when using a linear regression model or a polynomial regression model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Aanes, Fredrik Lohne
Format:	Preprint
Published:	2025
Subjects:	Methodology Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Approximate Shapley value estimation using sampling without replacement and variance estimation via the new Symmetric bootstrap and the Doubled half bootstrap
by: Aanes, Fredrik Lohne
Published: (2026)

Convex estimation of Gaussian graphical regression models with covariates
by: Liu, Ruobin, et al.
Published: (2024)

A least distance estimator for a multivariate regression model using deep neural networks
by: Shin, Jungmin, et al.
Published: (2024)

A feature selection method based on Shapley values robust to concept shift in regression
by: Sebastián, Carlos, et al.
Published: (2023)

A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
by: Kim, Youngseok, et al.
Published: (2022)

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

Semi-supervised learning for linear extremile regression
by: Jiang, Rong, et al.
Published: (2025)

Augmented balancing weights as linear regression
by: Bruns-Smith, David, et al.
Published: (2023)

BARTSIMP: flexible spatial covariate modeling and prediction using Bayesian additive regression trees
by: Jiang, Alex Ziyu, et al.
Published: (2023)

Personalizing black-box models for nonparametric regression with minimax optimality
by: Li, Sai, et al.
Published: (2026)

Approximate Bayesian inference for cumulative probit regression models
by: Aliverti, Emanuele
Published: (2025)

An interpretable neural network-based non-proportional odds model for ordinal regression
by: Okuno, Akifumi, et al.
Published: (2023)

Debiased high-dimensional regression calibration for errors-in-variables log-contrast models
by: Zhao, Huali, et al.
Published: (2024)

Fréchet random forests for metric space valued regression with non euclidean predictors
by: Capitaine, Louis, et al.
Published: (2019)

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

Multivariate outlier explanations using Shapley values and Mahalanobis distances
by: Mayrhofer, Marcus, et al.
Published: (2022)

Network-based Neighborhood regression
by: Zhen, Yaoming, et al.
Published: (2024)

Progression: an extrapolation principle for regression
by: Buriticá, Gloria, et al.
Published: (2024)

A semi-supervised learning using over-parameterized regression
by: Hagiwara, Katsuyuki
Published: (2024)

Adversarial learning for nonparametric regression: Minimax rate and adaptive estimation
by: Peng, Jingfu, et al.
Published: (2025)

Efficient nonparametric statistical inference on population feature importance using Shapley values
by: Williamson, Brian D., et al.
Published: (2020)

Robust non-parametric mortality and fertility modelling and forecasting: Gaussian process regression approaches
by: Lam, Ka Kin, et al.
Published: (2021)

Transfer learning for functional linear regression via control variates
by: Yang, Yuping, et al.
Published: (2026)

Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products
by: Papacharalampous, Georgia, et al.
Published: (2024)

Overview and practical recommendations on using Shapley Values for identifying predictive biomarkers via CATE modeling
by: Svensson, David, et al.
Published: (2025)

On relative universality, regression operator, and conditional independence
by: Li, Bing, et al.
Published: (2025)

Overcoming error-in-variable problem in data-driven model discovery by orthogonal distance regression
by: Fung, Lloyd
Published: (2025)

Neural interval-censored survival regression with feature selection
by: Meixide, Carlos García, et al.
Published: (2022)

Dimension-free bounds in high-dimensional linear regression via error-in-operator approach
by: Noskov, Fedor, et al.
Published: (2025)

Feature-free regression kriging
by: Luo, Peng, et al.
Published: (2025)

flexBART: Flexible Bayesian regression trees with categorical predictors
by: Deshpande, Sameer K.
Published: (2022)

Wasserstein F-tests for Fréchet regression on Bures-Wasserstein manifolds
by: Xu, Haoshu, et al.
Published: (2024)

The MELODIC family for simultaneous binary logistic regression in a reduced space
by: de Rooij, Mark, et al.
Published: (2021)

Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?
by: John, Luis H., et al.
Published: (2020)

IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark
by: Johansson, Fredrik D.
Published: (2024)

Propensity score models are better when post-calibrated
by: Gutman, Rom, et al.
Published: (2022)

Bayesian Federated Inference for regression models based on non-shared multicenter data sets from heterogeneous populations
by: Jonker, Marianne A, et al.
Published: (2024)

An integrated perspective of robustness in regression through the lens of the bias-variance trade-off
by: Okuno, Akifumi
Published: (2024)

Renewable estimation in linear expectile regression models with streaming data sets
by: Cao, Wei, et al.
Published: (2026)

On metric choice in dimension reduction for Fréchet regression
by: Soale, Abdul-Nasah, et al.
Published: (2024)