LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method
Fuente:
arXiv
Saved in:
| Main Authors: | Madakkatel, Iqbal, Hyppönen, Elina |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Machine learning to discover factors predicting volume of white matter hyperintensities: Insights from the UK Biobank
by: Yigizie Yeshaw, et al.
Published: (2025)
by: Yigizie Yeshaw, et al.
Published: (2025)
MinShap: A Modified Shapley Value Approach for Feature Selection
by: Zheng, Chenghui, et al.
Published: (2026)
by: Zheng, Chenghui, et al.
Published: (2026)
Feature Inference Attack on Shapley Values
by: Luo, Xinjian, et al.
Published: (2024)
by: Luo, Xinjian, et al.
Published: (2024)
Energy-Based Model for Accurate Estimation of Shapley Values in Feature Attribution
by: Lu, Cheng, et al.
Published: (2024)
by: Lu, Cheng, et al.
Published: (2024)
An Experiment on Feature Selection using Logistic Regression
by: Islam, Raisa, et al.
Published: (2024)
by: Islam, Raisa, et al.
Published: (2024)
FW-Shapley: Real-time Estimation of Weighted Shapley Values
by: Panda, Pranoy, et al.
Published: (2025)
by: Panda, Pranoy, et al.
Published: (2025)
Priority-Aware Shapley Value
by: Lee, Kiljae, et al.
Published: (2026)
by: Lee, Kiljae, et al.
Published: (2026)
RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
by: Chowdhury, Tanya, et al.
Published: (2024)
by: Chowdhury, Tanya, et al.
Published: (2024)
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
by: Fan, Wangxuan, et al.
Published: (2025)
by: Fan, Wangxuan, et al.
Published: (2025)
Probabilistic Shapley Value Modeling and Inference
by: Ketenci, Mert, et al.
Published: (2024)
by: Ketenci, Mert, et al.
Published: (2024)
On Model Extrapolation in Marginal Shapley Values
by: Rozenfeld, Ilya
Published: (2024)
by: Rozenfeld, Ilya
Published: (2024)
Prediction via Shapley Value Regression
by: Alkhatib, Amr, et al.
Published: (2025)
by: Alkhatib, Amr, et al.
Published: (2025)
TokenShapley: Token Level Context Attribution with Shapley Value
by: Xiao, Yingtai, et al.
Published: (2025)
by: Xiao, Yingtai, et al.
Published: (2025)
On the Inflation of KNN-Shapley Value
by: Yang, Ziao, et al.
Published: (2024)
by: Yang, Ziao, et al.
Published: (2024)
An Odd Estimator for Shapley Values
by: Fumagalli, Fabian, et al.
Published: (2026)
by: Fumagalli, Fabian, et al.
Published: (2026)
Suboptimal Shapley Value Explanations
by: Lu, Xiaolei
Published: (2025)
by: Lu, Xiaolei
Published: (2025)
Amortized Linear-time Exact Shapley Value for Product-Kernel Methods
by: Mohammadi, Majid, et al.
Published: (2025)
by: Mohammadi, Majid, et al.
Published: (2025)
Stabilizing Estimates of Shapley Values with Control Variates
by: Goldwasser, Jeremy, et al.
Published: (2023)
by: Goldwasser, Jeremy, et al.
Published: (2023)
Explainable Fraud Detection with GNNExplainer and Shapley Values
by: Dao, Ngoc Hieu
Published: (2025)
by: Dao, Ngoc Hieu
Published: (2025)
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them
by: Olsen, Lars Henry Berge, et al.
Published: (2023)
by: Olsen, Lars Henry Berge, et al.
Published: (2023)
Explaining Drift using Shapley Values
by: Edakunni, Narayanan U., et al.
Published: (2024)
by: Edakunni, Narayanan U., et al.
Published: (2024)
Exactly Computing do-Shapley Values
by: Witter, R. Teal, et al.
Published: (2026)
by: Witter, R. Teal, et al.
Published: (2026)
Generalized Priority-Aware Shapley Value
by: Lee, Kiljae, et al.
Published: (2026)
by: Lee, Kiljae, et al.
Published: (2026)
Tractable Shapley Values and Interactions via Tensor Networks
by: Heidari, Farzaneh, et al.
Published: (2025)
by: Heidari, Farzaneh, et al.
Published: (2025)
Provably Adaptive Linear Approximation for the Shapley Value and Beyond
by: Li, Weida, et al.
Published: (2026)
by: Li, Weida, et al.
Published: (2026)
Classification with Deep Neural Networks and Logistic Loss
by: Zhang, Zihan, et al.
Published: (2023)
by: Zhang, Zihan, et al.
Published: (2023)
Prompt Valuation Based on Shapley Values
by: Liu, Hanxi, et al.
Published: (2023)
by: Liu, Hanxi, et al.
Published: (2023)
Faithful Group Shapley Value
by: Lee, Kiljae, et al.
Published: (2025)
by: Lee, Kiljae, et al.
Published: (2025)
A Comprehensive Study of Shapley Value in Data Analytics
by: Lin, Hong, et al.
Published: (2024)
by: Lin, Hong, et al.
Published: (2024)
DeepCSHAP: Utilizing Shapley Values to Explain Deep Complex-Valued Neural Networks
by: Eilers, Florian, et al.
Published: (2024)
by: Eilers, Florian, et al.
Published: (2024)
A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values
by: Beechey, Daniel, et al.
Published: (2025)
by: Beechey, Daniel, et al.
Published: (2025)
Shapley Values: Paired-Sampling Approximations
by: Mayer, Michael, et al.
Published: (2025)
by: Mayer, Michael, et al.
Published: (2025)
Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values
by: Witter, R. Teal, et al.
Published: (2025)
by: Witter, R. Teal, et al.
Published: (2025)
Explaining Temporal Graph Predictions With Shapley Values
by: Sussek, Lea-Marie, et al.
Published: (2026)
by: Sussek, Lea-Marie, et al.
Published: (2026)
Shapley-Value-Based Graph Sparsification for GNN Inference
by: Akkas, Selahattin, et al.
Published: (2025)
by: Akkas, Selahattin, et al.
Published: (2025)
On the Computational Tractability of the (Many) Shapley Values
by: Marzouk, Reda, et al.
Published: (2025)
by: Marzouk, Reda, et al.
Published: (2025)
The Space Complexity of Approximating Logistic Loss
by: Dexter, Gregory, et al.
Published: (2024)
by: Dexter, Gregory, et al.
Published: (2024)
InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly
by: Enouen, James, et al.
Published: (2025)
by: Enouen, James, et al.
Published: (2025)
Shapley-Inspired Feature Weighting in $k$-means with No Additional Hyperparameters
by: Fawley, Richard J., et al.
Published: (2025)
by: Fawley, Richard J., et al.
Published: (2025)
Phenome‐wide association study of ovarian cancer identifies common comorbidities and reveals shared genetics with complex diseases and biomarkers
by: Anwar Mulugeta, et al.
Published: (2024)
by: Anwar Mulugeta, et al.
Published: (2024)
Similar Items
-
Machine learning to discover factors predicting volume of white matter hyperintensities: Insights from the UK Biobank
by: Yigizie Yeshaw, et al.
Published: (2025) -
MinShap: A Modified Shapley Value Approach for Feature Selection
by: Zheng, Chenghui, et al.
Published: (2026) -
Feature Inference Attack on Shapley Values
by: Luo, Xinjian, et al.
Published: (2024) -
Energy-Based Model for Accurate Estimation of Shapley Values in Feature Attribution
by: Lu, Cheng, et al.
Published: (2024) -
An Experiment on Feature Selection using Logistic Regression
by: Islam, Raisa, et al.
Published: (2024)