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2009.05487
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The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples
Minds and Machines (MM), 2020
11 September 2020
Timo Freiesleben
GAN
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Papers citing
"The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples"
40 / 40 papers shown
Generalizability vs. Counterfactual Explainability Trade-Off
Fabiano Veglianti
Flavio Giorgi
Fabrizio Silvestri
Gabriele Tolomei
275
0
0
29 May 2025
Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks
Jeremy Goldwasser
Giles Hooker
AAML
340
0
0
21 Apr 2025
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
Jonas Teufel
Annika Leinweber
Pascal Friederich
465
4
0
03 Apr 2025
Guidelines For The Choice Of The Baseline in XAI Attribution Methods
Cristian Morasso
Giorgio Dolci
I. Galazzo
Sergey M. Plis
Gloria Menegaz
341
3
0
25 Mar 2025
All You Need for Counterfactual Explainability Is Principled and Reliable Estimate of Aleatoric and Epistemic Uncertainty
Kacper Sokol
Eyke Hüllermeier
470
4
0
24 Feb 2025
Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization
Keita Kinjo
411
3
0
10 Jan 2025
Causal Interpretability for Adversarial Robustness: A Hybrid Generative Classification Approach
Chunheng Zhao
P. Pisu
G. Comert
N. Begashaw
Varghese Vaidyan
Nina Christine Hubig
AAML
313
1
0
28 Dec 2024
Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations
BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackBoxNLP), 2024
Supriya Manna
Niladri Sett
AAML
421
3
0
26 Sep 2024
CF-OPT: Counterfactual Explanations for Structured Prediction
Germain Vivier-Ardisson
Alexandre Forel
Axel Parmentier
Thibaut Vidal
OffRL
CML
BDL
448
3
0
28 May 2024
Trustworthy Actionable Perturbations
International Conference on Machine Learning (ICML), 2024
Jesse Friedbaum
Sudarshan Adiga
Ravi Tandon
AAML
344
3
0
18 May 2024
Do Counterfactual Examples Complicate Adversarial Training?
Eric C. Yeats
Cameron Darwin
Eduardo Ortega
Frank Liu
Hai Li
DiffM
402
1
0
16 Apr 2024
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
Susanne Dandl
Kristin Blesch
Timo Freiesleben
Gunnar Konig
Jan Kapar
J. Herbinger
Marvin N. Wright
AAML
352
9
0
04 Apr 2024
Machine Learning Robustness: A Primer
Houssem Ben Braiek
Foutse Khomh
AAML
OOD
592
24
0
01 Apr 2024
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
Data mining and knowledge discovery (DMKD), 2024
A. Buliga
Chiara Di Francescomarino
Chiara Ghidini
Ivan Donadello
F. Maggi
228
4
0
18 Mar 2024
Towards Non-Adversarial Algorithmic Recourse
Tobias Leemann
Martin Pawelczyk
Bardh Prenkaj
Gjergji Kasneci
AAML
327
3
0
15 Mar 2024
SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledge
L. Fenaux
Florian Kerschbaum
AAML
384
0
0
22 Feb 2024
Fragility, Robustness and Antifragility in Deep Learning
Artificial Intelligence (AIJ), 2023
Chandresh Pravin
Ivan Martino
Giuseppe Nicosia
Varun Ojha
338
6
0
15 Dec 2023
Artificial Neural Nets and the Representation of Human Concepts
Timo Freiesleben
NAI
395
5
0
08 Dec 2023
D4Explainer: In-Distribution GNN Explanations via Discrete Denoising Diffusion
Jialin Chen
Shirley Wu
Abhijit Gupta
Rex Ying
DiffM
303
8
0
30 Oct 2023
Generating collective counterfactual explanations in score-based classification via mathematical optimization
E. Carrizosa
Jasone Ramírez-Ayerbe
Dolores Romero Morales
266
33
0
19 Oct 2023
On the Trade-offs between Adversarial Robustness and Actionable Explanations
AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2023
Satyapriya Krishna
Chirag Agarwal
Himabindu Lakkaraju
AAML
331
1
0
28 Sep 2023
Adaptive Adversarial Training Does Not Increase Recourse Costs
AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2023
Ian Hardy
Jayanth Yetukuri
Yang Liu
AAML
208
1
0
05 Sep 2023
Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluation
Philipp Vaeth
Alexander M. Fruehwald
Benjamin Paassen
Magda Gregorova
DiffM
324
6
0
11 Aug 2023
Towards Explainable Evaluation Metrics for Machine Translation
Journal of machine learning research (JMLR), 2023
Christoph Leiter
Piyawat Lertvittayakumjorn
M. Fomicheva
Wei Zhao
Yang Gao
Steffen Eger
ELM
367
27
0
22 Jun 2023
The Risks of Recourse in Binary Classification
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
H. Fokkema
Damien Garreau
T. Erven
FaML
325
8
0
01 Jun 2023
counterfactuals: An R Package for Counterfactual Explanation Methods
Susanne Dandl
Andreas Hofheinz
Martin Binder
J. Herbinger
Giuseppe Casalicchio
353
2
0
13 Apr 2023
Clarity: an improved gradient method for producing quality visual counterfactual explanations
Claire Theobald
Frédéric Pennerath
Brieuc Conan-Guez
Miguel Couceiro
Amedeo Napoli
BDL
294
1
0
22 Nov 2022
Improvement-Focused Causal Recourse (ICR)
AAAI Conference on Artificial Intelligence (AAAI), 2022
Gunnar Konig
Timo Freiesleben
Moritz Grosse-Wentrup
CML
277
21
0
27 Oct 2022
Green Learning: Introduction, Examples and Outlook
Journal of Visual Communication and Image Representation (JVCIR), 2022
C.-C. Jay Kuo
A. Madni
319
100
0
03 Oct 2022
Formalising the Robustness of Counterfactual Explanations for Neural Networks
AAAI Conference on Artificial Intelligence (AAAI), 2022
Junqi Jiang
Francesco Leofante
Antonio Rago
Francesca Toni
AAML
412
34
0
31 Aug 2022
Testing robustness of predictions of trained classifiers against naturally occurring perturbations
S. Scher
A. Trugler
OOD
AAML
429
5
0
21 Apr 2022
Interpretation of Black Box NLP Models: A Survey
Shivani Choudhary
N. Chatterjee
S. K. Saha
FAtt
256
19
0
31 Mar 2022
Towards Explainable Evaluation Metrics for Natural Language Generation
Christoph Leiter
Piyawat Lertvittayakumjorn
M. Fomicheva
Wei Zhao
Yang Gao
Steffen Eger
AAML
ELM
288
22
0
21 Mar 2022
On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations
Artificial Intelligence (AIJ), 2022
M. Virgolin
Saverio Fracaros
CML
380
41
0
22 Jan 2022
DeDUCE: Generating Counterfactual Explanations Efficiently
Benedikt Höltgen
Lisa Schut
J. Brauner
Y. Gal
CML
192
6
0
29 Nov 2021
Contrastive Explanations for Model Interpretability
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
Alon Jacovi
Swabha Swayamdipta
Shauli Ravfogel
Yanai Elazar
Yejin Choi
Yoav Goldberg
528
115
0
02 Mar 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
International Conference on Artificial Intelligence and Statistics (AISTATS), 2021
Ana Lucic
Maartje ter Hoeve
Gabriele Tolomei
Maarten de Rijke
Fabrizio Silvestri
645
183
0
05 Feb 2021
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
ACM Computing Surveys (ACM CSUR), 2020
Sahil Verma
Varich Boonsanong
Minh Hoang
Keegan E. Hines
John P. Dickerson
Chirag Shah
CML
803
281
0
20 Oct 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey
ACM Computing Surveys (ACM CSUR), 2020
A. Serban
E. Poll
Joost Visser
AAML
580
83
0
07 Aug 2020
Generating Natural Adversarial Examples
Zhengli Zhao
Dheeru Dua
Sameer Singh
GAN
AAML
844
653
0
31 Oct 2017
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