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As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice.
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, and Isabel Valera · 2006
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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, and Isabel Valera · 2010
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Adversarial examples in the physical world, 2016
Alexey Kurakin, Ian Goodfellow, Samy Bengio, et al · 2016
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How we analyzed the compas recidivism algorithm
S Mattu, L Kirchner, and J Angwin · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Houdini: Fooling deep structured prediction models
Moustapha Cisse, Yossi Adi, Natalia Neverova, and Joseph Keshet · 2017
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UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
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Identity matters in deep learning
Moritz Hardt and Tengyu Ma · 2017
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A unified approach to interpreting model predictions
Scott Lundberg and Su-In Lee · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
The hidden assumptions behind counterfactual explanations and principal reasons
Solon Barocas, Andrew D Selbst, and Manish Raghavan · 2020
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Kieran Browne and Ben Swift · 2020
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Timo Freiesleben · 2020
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Explaining the explainer: A first theoretical analysis of lime
Damien Garreau and Ulrike Luxburg · 2020
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Learning model-agnostic counterfactual explanations for tabular data
Martin Pawelczyk, Klaus Broelemann, and Gjergji Kasneci · 2020
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Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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Generating natural adversarial examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh · 2018
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Imperceptible adversarial attacks on tabular data
Vincent Ballet, Xavier Renard, Jonathan Aigrain, Thibault Laugel, Pascal Frossard, and Marcin Detyniecki · 2019
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Shalmali Joshi, Oluwasanmi Koyejo, Warut Vijitbenjaronk, Been Kim, and Joydeep Ghosh · 2019
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
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Interpretable and interactive summaries ofactionable recourses
Kaivalya Rawal and Himabindu Lakkaraju · 2020
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Certified robustness to label-flipping attacks via randomized smoothing
Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, and Zico Kolter · 2020
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The philosophical basis of algorithmic recourse
Suresh Venkatasubramanian and Mark Alfano · 2020
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Counterfactual explanations for machine learning: A review
Sahil Verma, John Dickerson, and Keegan Hines · 2020
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Adversarial attacks for tabular data: Application to fraud detection and imbalanced data
Francesco Cartella, Orlando Anunciacao, Yuki Funabiki, Daisuke Yamaguchi, Toru Akishita, and Olivier Elshocht · 2021
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Regularisation of neural networks by enforcing lipschitz continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J Cree · 2021
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Algorithmic recourse: from counterfactual explanations to interventions
Amir-Hossein Karimi, Bernhard Schölkopf, and Isabel Valera · 2021
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Adversarial poisoning attacks and defense for general multi-class models based on synthetic reduced nearest neighbors
Pooya Tavallali, Vahid Behzadan, Peyman Tavallali, and Mukesh Singhal · 2021
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