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Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models.
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"Why Should I Trust You?": Explaining the Predictions of Any Classifier. In Proc. of KDD (KDD’16) . ACM, 1135–1144
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A Unified Approach to Interpreting Model Predictions
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Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
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Multi-Objective Counterfactual Explanations. In Proc. of PPSN (Lecture Notes in Computer Science, Vol. 12269) , Thomas Bäck, Mike Preuss, André H. Deutz, Hao Wang, Carola Doerr, Michael T. M. Emmerich, and Heike Trautmann (Eds.). Springer, 448–469
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Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
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DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization. In Proc. of IJCAI , Christian Bessiere (Ed.). ijcai.org, 2855–2862
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Exploration by Random Network Distillation
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Local Rule-Based Explanations of Black Box Decision Systems
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Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space. In 2018 AAAI Spring Symposium Series
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Efficient Search for Diverse Coherent Explanations. In Proc. of FAT* (FAT*’19) , Danah Boyd and Jamie H. Morgenstern (Eds.). ACM, 20–28
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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
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GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model’s Prediction. In Proc. of KDD (KDD’20) , Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 238–248
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Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations. In Proc. of FAT* (FAT*’20) , Mireille Hildebrandt, Carlos Castillo, L. Elisa Celis, Salvatore Ruggieri, Linnet Taylor, and Gabriela Zanfir-Fortuna (Eds.). ACM, 607–617
Ramaravind Kommiya Mothilal, Amit Sharma, and Chenhao Tan. 2020 · 2020
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Learning Model-Agnostic Counterfactual Explanations for Tabular Data. In Proc. of WWW (TheWebConf’20) , Yennun Huang, Irwin King, Tie-Yan Liu, and Maarten van Steen (Eds.). ACM / IW3C2, 3126–3132
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci. 2020 · 2020
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FACE: Feasible and Actionable Counterfactual Explanations. In Proc. of AIES (AIES’20) . 344–350
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, and Peter Flach. 2020 · 2020
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Counterfactual Explanations for Machine Learning: A Review
Sahil Verma, John Dickerson, and Keegan Hines. 2020 · 2020
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Global Health Workforce Statistics
World Health Organization. 2020 · 2020
Later among the works it cites.
A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. 2021 · 2020
Later among the works it cites.
Counterfactual Explanations for Arbitrary Regression Models
Thomas Spooner, Danial Dervovic, Jason Long, Jon Shepard, Jiahao Chen, and Daniele Magazzeni. 2021 · 2021
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A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence
Ilia Stepin, Jose M Alonso, Alejandro Catala, and Martín Pereira-Fariña. 2021 · 2021
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Counterfactual Explanations and How to Find Them: Literature Review and Benchmarking
Riccardo Guidotti. 2022 · 2022
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The Skyline of Counterfactual Explanations for Machine Learning Decision Models. In Proc. of CIKM (CIKM’21) . ACM, 2030–2039
Yongjie Wang, Qinxu Ding, Ke Wang, Yue Liu, Xingyu Wu, Jinglong Wang, Yong Liu, and Chunyan Miao. 2021 · 2039
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