ECINN: Efficient Counterfactuals from Invertible Neural Networks

ECINN: Efficient Counterfactuals from Invertible Neural Networks

25 March 2021

Frederik Hvilshoj

Alexandros Iosifidis

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

11 / 11 papers shown

Title
Explainable AI needs formal notions of explanation correctness Stefan Haufe Rick Wilming Benedict Clark Rustam Zhumagambetov Danny Panknin Ahcène Boubekki XAI 31 1 0 22 Sep 2024
Global Counterfactual Directions Bartlomiej Sobieski P. Biecek DiffM 58 5 0 18 Apr 2024
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows Tri Dung Duong Qian Li Guandong Xu OOD 37 7 0 26 Mar 2023
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 36 12 0 19 Aug 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 29 14 0 25 Apr 2022
Diffusion Models for Counterfactual Explanations Guillaume Jeanneret Loïc Simon F. Jurie DiffM 32 55 0 29 Mar 2022
Diffusion Causal Models for Counterfactual Estimation Pedro Sanchez Sotirios A. Tsaftaris DiffM BDL 35 69 0 21 Feb 2022
When less is more: Simplifying inputs aids neural network understanding R. Schirrmeister Rosanne Liu Sara Hooker T. Ball 24 5 0 14 Jan 2022
Fighting Money Laundering with Statistics and Machine Learning R. Jensen Alexandros Iosifidis 36 13 0 11 Jan 2022
On Quantitative Evaluations of Counterfactuals Frederik Hvilshoj Alexandros Iosifidis Ira Assent 19 10 0 30 Oct 2021
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 57 93 0 05 Mar 2020