True to the Model or True to the Data?

29 June 2020

Papers citing "True to the Model or True to the Data?"

48 / 98 papers shown

Title
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams Fabian Fumagalli Maximilian Muschalik Eyke Hüllermeier Barbara Hammer 66 22 0 05 Sep 2022
Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation Andrew Herren P. R. Hahn FAtt 53 10 0 21 Aug 2022
Unifying local and global model explanations by functional decomposition of low dimensional structures M. Hiabu Josephine T. Meyer Marvin N. Wright FAtt 82 21 0 12 Aug 2022
Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights Tianshu Feng Zhipu Zhou Tarun Joshi V. Nair FAtt 48 5 0 12 Aug 2022
Learning Unsupervised Hierarchies of Audio Concepts Darius Afchar Romain Hennequin Vincent Guigue 84 2 0 21 Jul 2022
Algorithms to estimate Shapley value feature attributions Hugh Chen Ian Covert Scott M. Lundberg Su-In Lee TDI FAtt 98 240 0 15 Jul 2022
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing Jacopo Teneggi Beepul Bharti Yaniv Romano Jeremias Sulam FAtt 103 5 0 14 Jul 2022
Shapley Computations Using Surrogate Model-Based Trees Zhipu Zhou Jie Chen Linwei Hu 44 0 0 11 Jul 2022
Inherent Inconsistencies of Feature Importance Nimrod Harel Uri Obolski Ran Gilad-Bachrach FAtt 28 0 0 16 Jun 2022
Order-sensitive Shapley Values for Evaluating Conceptual Soundness of NLP Models Kaiji Lu Anupam Datta 50 0 0 01 Jun 2022
Explaining Preferences with Shapley Values Robert Hu Siu Lun Chau Jaime Ferrando Huertas Dino Sejdinovic TDI FAtt 80 6 0 26 May 2022
Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees Joseph Janssen Vincent Guan Elina Robeva 66 7 0 21 Apr 2022
Faith-Shap: The Faithful Shapley Interaction Index Che-Ping Tsai Chih-Kuan Yeh Pradeep Ravikumar TDI 114 55 0 02 Mar 2022
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations Chih-Kuan Yeh Kuan-Yun Lee Frederick Liu Pradeep Ravikumar FAtt TDI 52 9 0 24 Feb 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 69 23 0 22 Feb 2022
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs Alan Perotti P. Bajardi Francesco Bonchi Andre' Panisson FAtt 36 8 0 17 Feb 2022
Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap Carlos Mougan Dan Saattrup Nielsen 102 15 0 27 Jan 2022
Explainability in Music Recommender Systems Darius Afchar Alessandro B. Melchiorre Markus Schedl Romain Hennequin Elena V. Epure Manuel Moussallam 105 50 0 25 Jan 2022
Exact Shapley Values for Local and Model-True Explanations of Decision Tree Ensembles Thomas W. Campbell H. Roder R. Georgantas J. Roder FedML TDI FAtt 65 16 0 16 Dec 2021
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features Lars Henry Berge Olsen I. Glad Martin Jullum K. Aas TDI FAtt 109 17 0 26 Nov 2021
Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics A. Miroshnikov Konstandinos Kotsiopoulos Ryan Franks Arjun Ravi Kannan 41 5 0 19 Nov 2021
Counterfactual Shapley Additive Explanations Emanuele Albini Jason Long Danial Dervovic Daniele Magazzeni 107 51 0 27 Oct 2021
RKHS-SHAP: Shapley Values for Kernel Methods Siu Lun Chau Robert Hu Javier I. González Dino Sejdinovic FAtt 83 20 0 18 Oct 2021
Unraveling the graph structure of tabular data through Bayesian and spectral analysis B. M. F. Resende Eric K. Tokuda L. D. F. Costa CML 63 2 0 04 Oct 2021
Fast TreeSHAP: Accelerating SHAP Value Computation for Trees Jilei Yang FAtt 105 37 0 20 Sep 2021
Model Explanations via the Axiomatic Causal Lens Gagan Biradar Vignesh Viswanathan Yair Zick XAI CML 84 1 0 08 Sep 2021
Bringing a Ruler Into the Black Box: Uncovering Feature Impact from Individual Conditional Expectation Plots A. Yeh A. Ngo FAtt 42 4 0 06 Sep 2021
Data-driven advice for interpreting local and global model predictions in bioinformatics problems Markus Loecher Qi Wu FAtt 17 1 0 13 Aug 2021
Seven challenges for harmonizing explainability requirements Jiahao Chen Victor Storchan 71 8 0 11 Aug 2021
Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition Jan Ittner Lukasz Bolikowski Konstantin Hemker Ricardo Kennedy FAtt 46 7 0 26 Jul 2021
A Decision Support System for Safer Airplane Landings: Predicting Runway Conditions Using XGBoost and Explainable AI A. Midtfjord R. D. Bin A. Huseby 28 27 0 01 Jul 2021
On Locality of Local Explanation Models Sahra Ghalebikesabi Lucile Ter-Minassian Karla Diaz-Ordaz Chris Holmes FedML FAtt 73 40 0 24 Jun 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White Willie Neiswanger 115 67 0 23 Jun 2021
groupShapley: Efficient prediction explanation with Shapley values for feature groups Martin Jullum Annabelle Redelmeier K. Aas TDI FAtt 80 22 0 23 Jun 2021
Accurate Shapley Values for explaining tree-based models Salim I. Amoukou Nicolas Brunel Tangi Salaun TDI FAtt 69 15 0 07 Jun 2021
Do not explain without context: addressing the blind spot of model explanations Katarzyna Wo'znica Katarzyna Pkekala Hubert Baniecki Wojciech Kretowicz El.zbieta Sienkiewicz P. Biecek 61 1 0 28 May 2021
Explaining a Series of Models by Propagating Shapley Values Hugh Chen Scott M. Lundberg Su-In Lee TDI FAtt 103 139 0 30 Apr 2021
Fast Hierarchical Games for Image Explanations Jacopo Teneggi Alexandre Luster Jeremias Sulam FAtt 54 21 0 13 Apr 2021
The Shapley Value of coalition of variables provides better explanations Salim I. Amoukou Nicolas Brunel Tangi Salaun FAtt TDI 51 5 0 24 Mar 2021
Explaining predictive models using Shapley values and non-parametric vine copulas K. Aas T. Nagler Martin Jullum Anders Løland FAtt 72 20 0 12 Feb 2021
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 151 252 0 21 Nov 2020
Wasserstein-based fairness interpretability framework for machine learning models A. Miroshnikov Konstandinos Kotsiopoulos Ryan Franks Arjun Ravi Kannan FAtt 81 15 0 06 Nov 2020
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions Jiaxuan Wang Jenna Wiens Scott M. Lundberg FAtt 129 90 0 27 Oct 2020
Marginal Contribution Feature Importance -- an Axiomatic Approach for The Natural Case Amnon Catav Boyang Fu J. Ernst S. Sankararaman Ran Gilad-Bachrach FAtt 41 3 0 15 Oct 2020
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models Christoph Molnar Gunnar Konig J. Herbinger Timo Freiesleben Susanne Dandl Christian A. Scholbeck Giuseppe Casalicchio Moritz Grosse-Wentrup B. Bischl FAtt AI4CE 85 140 0 08 Jul 2020
Model Explanations with Differential Privacy Neel Patel Reza Shokri Yair Zick SILM FedML 143 32 0 16 Jun 2020
Shapley explainability on the data manifold Christopher Frye Damien de Mijolla T. Begley Laurence Cowton Megan Stanley Ilya Feige FAtt TDI 87 103 0 01 Jun 2020
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K. Aas Martin Jullum Anders Løland FAtt TDI 90 635 0 25 Mar 2019