Decorrelated feature importance from local sample weighting | Paper Citation Record

Observation 87ef704d-d6e1-45c4-a5f3-df553763aed9 · outbound

This paper cites Importance of interpretability in healthcare,.

Decorrelated feature importance from local sample weighting Importance of interpretability in healthcare,

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This paper cites On the impor- tance of interpretable machine learning predictions to inform clinical decision making in oncology,.

Decorrelated feature importance from local sample weighting On the impor- tance of interpretable machine learning predictions to inform clinical decision making in oncology,

Reference 2

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This paper cites Visualization of neural networks using saliency maps,.

Decorrelated feature importance from local sample weighting Visualization of neural networks using saliency maps,

Reference 3

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This paper cites Hastie, R.

Reference 4

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

Decorrelated feature importance from local sample weighting Explaining prediction models and individual predic- tions with feature contributions,

Reference 5

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This paper cites Random Forests,.

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This paper cites Distribution-Free Predictive Inference for Regression,.

Decorrelated feature importance from local sample weighting Distribution-Free Predictive Inference for Regression,

Reference 7

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This paper cites Bias in random forest variable importance measures: Illustrations, sources and a solution,.

Decorrelated feature importance from local sample weighting Bias in random forest variable importance measures: Illustrations, sources and a solution,

Reference 8

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This paper cites Disentangling Interactions and Depen- dencies in Feature Attribution,.

Decorrelated feature importance from local sample weighting Disentangling Interactions and Depen- dencies in Feature Attribution,

Reference 9

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This paper cites Do little interactions get lost in dark random forests?.

Decorrelated feature importance from local sample weighting Do little interactions get lost in dark random forests?

Reference 10

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This paper cites On the trustworthiness of tree ensemble explainability methods,.

Decorrelated feature importance from local sample weighting On the trustworthiness of tree ensemble explainability methods,

Reference 11

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Observation afd33151-e942-4658-8ee0-69d480a8ca5e · outbound

This paper cites Conditional vari- able importance for random forests,.

Decorrelated feature importance from local sample weighting Conditional vari- able importance for random forests,

Reference 12

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This paper cites Correlation and variable importance in random forests,.

Decorrelated feature importance from local sample weighting Correlation and variable importance in random forests,

Reference 13

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Observation 8c309036-c6ea-4646-b01a-f7b348393b12 · outbound

This paper cites Breiman, F.

Reference 14

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This paper cites Stable learning establishes some common ground between causal inference and machine learning,.

Decorrelated feature importance from local sample weighting Stable learning establishes some common ground between causal inference and machine learning,

Reference 15

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Observation 0dfd9a10-8ec8-4f60-bb45-980ad58b0ca4 · outbound

This paper cites Stable Learning via Differ- entiated Variable Decorrelation,.

Decorrelated feature importance from local sample weighting Stable Learning via Differ- entiated Variable Decorrelation,

Reference 16

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This paper cites Stable Learning via Sample Reweighting,.

Decorrelated feature importance from local sample weighting Stable Learning via Sample Reweighting,

Reference 17

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Observation d9fe9f41-4674-47cb-bacd-9b7a8624fa42 · outbound

This paper cites Stable Learning via Sparse Variable Independence,.

Decorrelated feature importance from local sample weighting Stable Learning via Sparse Variable Independence,

Reference 18

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This paper cites Stable Learning via Triplex Learning,.

Decorrelated feature importance from local sample weighting Stable Learning via Triplex Learning,

Reference 19

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Observation d9c3ea43-2276-4ee4-b152-588f85fe63b7 · outbound

This paper cites Stable Prediction with Model Mis- specification and Agnostic Distribution Shift,.

Decorrelated feature importance from local sample weighting Stable Prediction with Model Mis- specification and Agnostic Distribution Shift,

Reference 20

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This paper cites Propensity Score Stratification Methods for Continuous Treatments,.

Decorrelated feature importance from local sample weighting Propensity Score Stratification Methods for Continuous Treatments,

Reference 21

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This paper cites Marginal Structural Models and Causal Inference in Epidemiology,.

Decorrelated feature importance from local sample weighting Marginal Structural Models and Causal Inference in Epidemiology,

Reference 22

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This paper cites Decorrelated Variable Importance.

Reference 23

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This paper cites Learning Generalizable Agents via Saliency-Guided Features Decorrelation.

Reference 24

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This paper cites A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization.

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This paper cites Invariant Random Forest: Tree-Based Model Solution for OOD Generalization,.

Decorrelated feature importance from local sample weighting Invariant Random Forest: Tree-Based Model Solution for OOD Generalization,

Reference 26

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This paper cites Deep Stable Learning for Out-Of-Distribution Generalization,.

Decorrelated feature importance from local sample weighting Deep Stable Learning for Out-Of-Distribution Generalization,

Reference 27

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This paper cites Panning for gold: ‘model-X’ knockoffs for high dimensional controlled variable selection,.

Decorrelated feature importance from local sample weighting Panning for gold: ‘model-X’ knockoffs for high dimensional controlled variable selection,

Reference 28

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This paper cites A Novel Random Forest Variant Based on Intervention Correlation Ratio,.

Decorrelated feature importance from local sample weighting A Novel Random Forest Variant Based on Intervention Correlation Ratio,

Reference 29

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This paper cites Training Diagonal Linear Networks with Stochastic Sharpness-Aware Minimization.

Reference 30

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This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Reference 31

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This paper cites Simplifying neural nets by discovering flat min- ima,.

Decorrelated feature importance from local sample weighting Simplifying neural nets by discovering flat min- ima,

Reference 32

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This paper cites Testing conditional independence in supervised learning algorithms,.

Decorrelated feature importance from local sample weighting Testing conditional independence in supervised learning algorithms,

Reference 33

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Reference 34

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

Decorrelated feature importance from local sample weighting Iterative random forests to discover predictive and stable high-order interactions,

Reference 35

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

Decorrelated feature importance from local sample weighting Provable boolean interaction recovery from tree ensemble obtained via random forests,

Reference 36

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This paper cites Scikit-learn: Machine learning in Python,.

Decorrelated feature importance from local sample weighting Scikit-learn: Machine learning in Python,

Reference 37

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This paper cites Large-scale machine learning with stochastic gradient descent,.

Decorrelated feature importance from local sample weighting Large-scale machine learning with stochastic gradient descent,

Reference 38

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This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Reference 39

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Reference 40

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This paper cites Gradient-based learning applied to document recognition,.

Decorrelated feature importance from local sample weighting Gradient-based learning applied to document recognition,

Reference 41

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This paper cites Goodfellow, Y.

Reference 42

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This paper cites Adam: A method for stochastic optimization,.

Decorrelated feature importance from local sample weighting Adam: A method for stochastic optimization,

Reference 43

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This paper cites Model-Agnostic Confidence Intervals for Fea- ture Importance: A Fast and Powerful Approach Using Minipatch Ensembles,.

Decorrelated feature importance from local sample weighting Model-Agnostic Confidence Intervals for Fea- ture Importance: A Fast and Powerful Approach Using Minipatch Ensembles,

Reference 44

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This paper cites an unresolved cited work.

Reference 45

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Observation 7769c372-bacb-4b7c-bf39-d2a3aa50b030 · outbound

This paper cites Optimization methods for large-scale ma- chine learning,.

Decorrelated feature importance from local sample weighting Optimization methods for large-scale ma- chine learning,

Reference 46

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This paper cites Moreover, the runtime complexity of the algorithm is given byO(KN k).

Decorrelated feature importance from local sample weighting Moreover, the runtime complexity of the algorithm is given byO(KN k)

Reference 47

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Observation 5414f2a7-c289-411a-8b25-1211f4e6e17b · outbound

This paper cites In combination with a sorting algorithm (which is ofO(N k logN k) complexity), we can compute the optimal splitting point in aO(N k logN k) time.

Decorrelated feature importance from local sample weighting In combination with a sorting algorithm (which is ofO(N k logN k) complexity), we can compute the optimal splitting point in aO(N k logN k) time

Reference 48

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Observation 4587cdb9-1201-43cc-a45c-89a4a290918b · outbound

This paper cites Likewise, in the for-loop, the setLand left child weightWcan be computed in O(Nk) time, whereas the remaining steps are of constant complexity.

Decorrelated feature importance from local sample weighting Likewise, in the for-loop, the setLand left child weightWcan be computed in O(Nk) time, whereas the remaining steps are of constant complexity

Reference 49

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Observation 4de403b1-e04a-46c4-8de8-8a0542e974a5 · outbound

This paper cites We therefore advice to restrict to chooseηwithin the interval(0, 2 3 ]for applications of losaw.

Decorrelated feature importance from local sample weighting We therefore advice to restrict to chooseηwithin the interval(0, 2 3 ]for applications of losaw

Reference 50

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This paper cites Each of these linear models requires the covariance matrix of theQadjustment features, so it suffices to compute this matrix a single time inO(Q 2Nk) complexity.

Decorrelated feature importance from local sample weighting Each of these linear models requires the covariance matrix of theQadjustment features, so it suffices to compute this matrix a single time inO(Q 2Nk) complexity

Reference 51

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