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ECINN: Efficient Counterfactuals from Invertible Neural Networks

25 March 2021

Papers citing "ECINN: Efficient Counterfactuals from Invertible Neural Networks"

25 / 25 papers shown

Title
Counterfactual Visual Explanation via Causally-Guided Adversarial Steering Yiran Qiao Disheng Liu Yiren Lu Yu Yin Mengnan Du Jing Ma GAN CML AAML 64 0 0 14 Jul 2025
Gumbel Counterfactual Generation From Language Models Shauli Ravfogel Anej Svete Vésteinn Snæbjarnarson Robert Bamler LRM CML 190 6 0 11 Nov 2024
TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE Emmanouil Panagiotou Manuel Heurich Tim Landgraf Eirini Ntoutsi LMTD 87 4 0 14 Oct 2024
Explainable AI needs formal notions of explanation correctness Stefan Haufe Rick Wilming Benedict Clark Rustam Zhumagambetov Danny Panknin Ahcène Boubekki XAI 150 2 0 22 Sep 2024
The Gaussian Discriminant Variational Autoencoder (GdVAE): A Self-Explainable Model with Counterfactual Explanations Anselm Haselhoff Kevin Trelenberg Fabian Küppers Jonas Schneider 88 3 0 19 Sep 2024
Global Counterfactual Directions Bartlomiej Sobieski P. Biecek DiffM 255 8 0 18 Apr 2024
Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset William Taylor-Melanson Zahra Sadeghi Stan Matwin CML 111 7 0 21 Jan 2024
Latent Diffusion Counterfactual Explanations Karim Farid Simon Schrodi Max Argus Thomas Brox DiffM 125 17 0 10 Oct 2023
Counterfactual Learning on Graphs: A Survey Zhimeng Guo Teng Xiao Zongyu Wu Charu C. Aggarwal Hui Liu Suhang Wang CML AI4CE 249 24 0 03 Apr 2023
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows Tri Dung Duong Qian Li Guandong Xu OOD 84 7 0 26 Mar 2023
Adversarial Counterfactual Visual Explanations Guillaume Jeanneret Loïc Simon F. Jurie DiffM 129 37 0 17 Mar 2023
On Modifying a Neural Network's Perception Manuel de Sousa Ribeiro João Leite AAML 92 1 0 05 Mar 2023
A Rigorous Study Of The Deep Taylor Decomposition Leon Sixt Tim Landgraf FAtt AAML 77 6 0 14 Nov 2022
Causality-Inspired Taxonomy for Explainable Artificial Intelligence Pedro C. Neto Tiago B. Gonccalves João Ribeiro Pinto W. Silva Ana F. Sequeira Arun Ross Jaime S. Cardoso XAI 168 15 0 19 Aug 2022
Diffeomorphic Counterfactuals with Generative Models Ann-Kathrin Dombrowski Jan E. Gerken Klaus-Robert Muller Pan Kessel DiffM BDL 169 19 0 10 Jun 2022
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset Leon Sixt M. Schuessler Oana-Iuliana Popescu Philipp Weiß Tim Landgraf FAtt 93 15 0 25 Apr 2022
Diffusion Models for Counterfactual Explanations Guillaume Jeanneret Loïc Simon F. Jurie DiffM 162 68 0 29 Mar 2022
Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals Simon Vandenhende D. Mahajan Filip Radenovic Deepti Ghadiyaram 148 35 0 24 Mar 2022
Diffusion Causal Models for Counterfactual Estimation Pedro Sanchez Sotirios A. Tsaftaris DiffM BDL 148 87 0 21 Feb 2022
When less is more: Simplifying inputs aids neural network understanding R. Schirrmeister Rosanne Liu Sara Hooker T. Ball 214 5 0 14 Jan 2022
Fighting Money Laundering with Statistics and Machine Learning R. Jensen Alexandros Iosifidis 163 20 0 11 Jan 2022
On Quantitative Evaluations of Counterfactuals Frederik Hvilshoj Alexandros Iosifidis Ira Assent 111 10 0 30 Oct 2021
Counterfactual Interventions Reveal the Causal Effect of Relative Clause Representations on Agreement Prediction Shauli Ravfogel Grusha Prasad Tal Linzen Yoav Goldberg 161 65 0 14 May 2021
Benchmarking and Survey of Explanation Methods for Black Box Models F. Bodria F. Giannotti Riccardo Guidotti Francesca Naretto D. Pedreschi S. Rinzivillo XAI 158 252 0 25 Feb 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 329 203 0 20 Oct 2020