Verified Training for Counterfactual Explanation Robustness under Data
Shift

Verified Training for Counterfactual Explanation Robustness under Data Shift

6 March 2024

Aws Albarghouthi

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Papers citing "Verified Training for Counterfactual Explanation Robustness under Data Shift"

19 / 19 papers shown

Title
On Minimizing the Impact of Dataset Shifts on Actionable Explanations Anna P. Meyer Dan Ley Suraj Srinivas Himabindu Lakkaraju FAtt 61 6 0 11 Jun 2023
Distributionally Robust Recourse Action D. Nguyen Ngoc H. Bui Viet Anh Nguyen 64 8 0 22 Feb 2023
Formalising the Robustness of Counterfactual Explanations for Neural Networks Junqi Jiang Francesco Leofante Antonio Rago Francesca Toni AAML 49 27 0 31 Aug 2022
Efficiently Training Low-Curvature Neural Networks Suraj Srinivas Kyle Matoba Himabindu Lakkaraju François Fleuret AAML 70 16 0 14 Jun 2022
Counterfactual Plans under Distributional Ambiguity N. Bui D. Nguyen Viet Anh Nguyen 89 25 0 29 Jan 2022
CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations Hangzhi Guo T. Nguyen A. Yadav OffRL 55 16 0 15 Sep 2021
Towards Robust and Reliable Algorithmic Recourse Sohini Upadhyay Shalmali Joshi Himabindu Lakkaraju 58 110 0 26 Feb 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 75 64 0 18 Dec 2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander DÁmour Katherine A. Heller D. Moldovan Ben Adlam B. Alipanahi ... Kellie Webster Steve Yadlowsky T. Yun Xiaohua Zhai D. Sculley OffRL 120 688 0 06 Nov 2020
Fairwashing Explanations with Off-Manifold Detergent Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski K. Müller Pan Kessel FAtt FaML 58 97 0 20 Jul 2020
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 79 821 0 06 Nov 2019
Predictive Multiplicity in Classification Charles Marx Flavio du Pin Calmon Berk Ustun 123 146 0 14 Sep 2019
Towards Stable and Efficient Training of Verifiably Robust Neural Networks Huan Zhang Hongge Chen Chaowei Xiao Sven Gowal Robert Stanforth Yue Liu Duane S. Boning Cho-Jui Hsieh AAML 80 349 0 14 Jun 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 91 322 0 27 May 2019
Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo Sunghwan Joo Taesup Moon AAML FAtt 95 205 0 06 Feb 2019
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 129 2,361 0 01 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,018 0 22 May 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,033 0 16 Feb 2016
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 314 7,316 0 20 Dec 2013

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