Inverse Classification for Comparison-based Interpretability in Machine Learning

22 December 2017

Papers citing "Inverse Classification for Comparison-based Interpretability in Machine Learning"

23 / 23 papers shown

Title
From Search To Sampling: Generative Models For Robust Algorithmic Recourse Prateek Garg Lokesh Nagalapatti Sunita Sarawagi 31 0 0 12 May 2025
A New Approach to Backtracking Counterfactual Explanations: A Causal Framework for Efficient Model Interpretability Pouria Fatemi Ehsan Sharifian Mohammad Hossein Yassaee 43 0 0 05 May 2025
ExpProof : Operationalizing Explanations for Confidential Models with ZKPs Chhavi Yadav Evan Monroe Laufer Dan Boneh Kamalika Chaudhuri 91 0 0 06 Feb 2025
Explaining Black-Box Models through Counterfactuals Patrick Altmeyer A. V. Deursen Cynthia C. S. Liem CML LRM 34 2 0 14 Aug 2023
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows Tri Dung Duong Qian Li Guandong Xu OOD 34 7 0 26 Mar 2023
RACCER: Towards Reachable and Certain Counterfactual Explanations for Reinforcement Learning Jasmina Gajcin Ivana Dusparic CML 24 3 0 08 Mar 2023
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions Julia El Zini Mohamad Mansour M. Awad 21 1 0 19 Jan 2023
Counterfactual Explanations for Support Vector Machine Models S. Salazar Samuel Denton Ansaf Salleb-Aouissi AAML FAtt 20 2 0 14 Dec 2022
Decomposing Counterfactual Explanations for Consequential Decision Making Martin Pawelczyk Lea Tiyavorabun Gjergji Kasneci CML 16 1 0 03 Nov 2022
Redefining Counterfactual Explanations for Reinforcement Learning: Overview, Challenges and Opportunities Jasmina Gajcin Ivana Dusparic CML OffRL 35 8 0 21 Oct 2022
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification Adrien Bennetot Gianni Franchi Javier Del Ser Raja Chatila Natalia Díaz Rodríguez AAML 27 29 0 26 Sep 2022
Counterfactual Explanations Using Optimization With Constraint Learning Donato Maragno Tabea E. Rober Ilker Birbil CML 55 10 0 22 Sep 2022
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse Martin Pawelczyk Teresa Datta Johannes van-den-Heuvel Gjergji Kasneci Himabindu Lakkaraju 19 38 0 13 Mar 2022
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data Raphael Mazzine David Martens 18 33 0 09 Jul 2021
Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications Yu-Liang Chou Catarina Moreira P. Bruza Chun Ouyang Joaquim A. Jorge CML 47 176 0 07 Mar 2021
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations Andrea Ferrario M. Loi 17 5 0 09 Oct 2020
Model extraction from counterfactual explanations Ulrich Aivodji Alexandre Bolot Sébastien Gambs MIACV MLAU 27 51 0 03 Sep 2020
On Counterfactual Explanations under Predictive Multiplicity Martin Pawelczyk Klaus Broelemann Gjergji Kasneci 25 85 0 23 Jun 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 57 93 0 05 Mar 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 24 337 0 14 Feb 2020
Questioning the AI: Informing Design Practices for Explainable AI User Experiences Q. V. Liao D. Gruen Sarah Miller 52 703 0 08 Jan 2020
Learning Model-Agnostic Counterfactual Explanations for Tabular Data Martin Pawelczyk Johannes Haug Klaus Broelemann Gjergji Kasneci OOD CML 30 199 0 21 Oct 2019
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 17 21 0 01 Apr 2019