S-CFE: Simple Counterfactual Explanations

21 October 2024

Papers citing "S-CFE: Simple Counterfactual Explanations"

22 / 22 papers shown

Title
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests Susanne Dandl Kristin Blesch Timo Freiesleben Gunnar Konig Jan Kapar B. Bischl Marvin N. Wright AAML 52 5 0 04 Apr 2024
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees Faisal Hamman Erfaun Noorani Saumitra Mishra Daniele Magazzeni Sanghamitra Dutta OOD AAML 52 33 0 19 May 2023
Improvement-Focused Causal Recourse (ICR) Gunnar Konig Timo Freiesleben Moritz Grosse-Wentrup CML 50 15 0 27 Oct 2022
Sparse and Imperceptible Adversarial Attack via a Homotopy Algorithm Mingkang Zhu Tianlong Chen Zhangyang Wang AAML 37 20 0 10 Jun 2021
Counterfactual Explanations Can Be Manipulated Dylan Slack Sophie Hilgard Himabindu Lakkaraju Sameer Singh 53 136 0 04 Jun 2021
Consequence-aware Sequential Counterfactual Generation Philip Naumann Eirini Ntoutsi OffRL 45 25 0 12 Apr 2021
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Ze Liu Yutong Lin Yue Cao Han Hu Yixuan Wei Zheng Zhang Stephen Lin B. Guo ViT 319 21,175 0 25 Mar 2021
Evaluating Robustness of Counterfactual Explanations André Artelt Valerie Vaquet Riza Velioglu Fabian Hinder Johannes Brinkrolf M. Schilling Barbara Hammer 69 46 0 03 Mar 2021
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 53 169 0 20 Oct 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 47 172 0 08 Oct 2020
Convex Density Constraints for Computing Plausible Counterfactual Explanations André Artelt Barbara Hammer 34 47 0 12 Feb 2020
Decisions, Counterfactual Explanations and Strategic Behavior Stratis Tsirtsis Manuel Gomez Rodriguez 94 60 0 11 Feb 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 261 42,038 0 03 Dec 2019
Sparse and Imperceivable Adversarial Attacks Francesco Croce Matthias Hein AAML 62 199 0 11 Sep 2019
Towards Robust, Locally Linear Deep Networks Guang-He Lee David Alvarez-Melis Tommi Jaakkola ODL 101 48 0 07 Jul 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 45 380 0 03 Jul 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 100 1,005 0 19 May 2019
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Pai-Shun Ting Karthikeyan Shanmugam Payel Das FAtt 89 587 0 21 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 73 2,332 0 01 Nov 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 168 8,513 0 16 Aug 2016
Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hamed Karimi J. Nutini Mark Schmidt 221 1,208 0 16 Aug 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 582 16,828 0 16 Feb 2016