Paper Citation Record · LEDGER

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest

As of 8 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2512.11081.

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pith.paper-citation-record.v1
2512.11081 v2

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Pith citing papers itemized under the disclosed page cap.

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Outbound references

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This paper cites MegaSNPHunter: A learning approach to detect disease predisposition SNPs and high level interactions in 27 REFERENCES REFERENCES genome wide association study,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest MegaSNPHunter: A learning approach to detect disease predisposition SNPs and high level interactions in 27 REFERENCES REFERENCES genome wide association study,

Reference 1

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This paper cites SNPInterForest: A new method for detecting epistatic interactions,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest SNPInterForest: A new method for detecting epistatic interactions,

Reference 2

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This paper cites Random forests,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Random forests,

Reference 3

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This paper cites Iterative random forests to discover predictive and stable high-order interactions,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Iterative random forests to discover predictive and stable high-order interactions,

Reference 4

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Kumbier, S

Reference 5

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This paper cites Learning epistatic polygenic phenotypes with Boolean interactions,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Learning epistatic polygenic phenotypes with Boolean interactions,

Reference 6

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This paper cites Epistasis regulates genetic control of cardiac hypertrophy,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Epistasis regulates genetic control of cardiac hypertrophy,

Reference 7

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This paper cites Provable Boolean interaction recovery from tree ensemble obtained via random forests,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Provable Boolean interaction recovery from tree ensemble obtained via random forests,

Reference 8

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This paper cites Representation Properties of Networks: Kolmogorov’s Theorem Is Irrelevant,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Representation Properties of Networks: Kolmogorov’s Theorem Is Irrelevant,

Reference 9

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This paper cites “Why Should I Trust You?.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest “Why Should I Trust You?

Reference 10

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This paper cites Analysis of regression in game theory approach,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Analysis of regression in game theory approach,

Reference 11

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This paper cites Explaining prediction models and individual predic- tions with feature contributions,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Explaining prediction models and individual predic- tions with feature contributions,

Reference 12

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This paper cites A unified approach to interpreting model predictions,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest A unified approach to interpreting model predictions,

Reference 13

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This paper cites An axiomatic approach to the concept of interaction among players in cooperative games,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest An axiomatic approach to the concept of interaction among players in cooperative games,

Reference 14

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This paper cites The Shapley Taylor Interaction Index,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest The Shapley Taylor Interaction Index,

Reference 15

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This paper cites From Shapley Values to Generalized Additive Models and back,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest From Shapley Values to Generalized Additive Models and back,

Reference 16

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This paper cites Faith-Shap: The Faithful Shapley Interaction Index,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Faith-Shap: The Faithful Shapley Interaction Index,

Reference 17

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Observation 2766a61a-6007-4e2b-a00d-e1b1bee28f39 · outbound

This paper cites Local MDI+: Local Feature Importances for Tree-Based Models.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Local MDI+: Local Feature Importances for Tree-Based Models

Reference 18

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This paper cites From local explanations to global understanding with explain- able AI for trees,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest From local explanations to global understanding with explain- able AI for trees,

Reference 19

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This paper cites SHAFF: Fast and consistent SHApley eFfect estimates via random forests,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest SHAFF: Fast and consistent SHApley eFfect estimates via random forests,

Reference 20

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Beyond TreeSHAP: Effi- cient Computation of Any-Order Shapley Interactions for Tree Ensembles,

Reference 21

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Definitions, methods, and applications in interpretable machine learning,

Reference 22

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This paper cites Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges,.

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges,

Reference 23

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Interpretable Machine Learning for Discovery: Sta- tistical Challenges and Opportunities,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Explaining the explainer: A first theoretical analysis of LIME,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Unbiased Measurement of Feature Importance in Tree-Based Methods

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Unbiased variable importance for random forests,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Mean decrease accuracy for random forests: Inconsistency, and a practical solution via the Sobol-MDA,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Statistical Inference for Variable Importance,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest A General Framework for Inference on Algorithm-Agnostic Variable Importance,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Testing conditional independence in supervised learning algorithms,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Unresolved cited work

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest SIRUS: Stable and Interpretable RUle Set for classification,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Breiman, J

Reference 34

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Large Scale Prediction with Decision Trees,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Analysis of a random forests model,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Estimation and Inference of Heterogeneous Treatment Effects using Random Forests,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Scikit-learn: Machine Learning in Python,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest How we analyzed the COMPAS re- cidivism algorithm,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Decomposing Global Feature Effects Based on Feature Interactions,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Hyperparameters and tuning strategies for random forest,

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Provable Recovery of Locally Important Signed Features and Interactions from Random Forest Available:http://jmlr.org/papers/v20/18-760.html

Reference 2019

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