A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
Fuente:
arXiv
Saved in:
| Main Authors: | Kostrzewa, Marcin, Zięba, Maciej, Stefanowski, Jerzy |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Counterfactual Explanations Under Concept Drift
by: Kostrzewa, Marcin, et al.
Published: (2026)
by: Kostrzewa, Marcin, et al.
Published: (2026)
Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
by: Wielopolski, Patryk, et al.
Published: (2024)
by: Wielopolski, Patryk, et al.
Published: (2024)
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
by: Stępka, Ignacy, et al.
Published: (2024)
by: Stępka, Ignacy, et al.
Published: (2024)
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
by: Furman, Oleksii, et al.
Published: (2024)
by: Furman, Oleksii, et al.
Published: (2024)
Towards plausibility in time series counterfactual explanations
by: Kostrzewa, Marcin, et al.
Published: (2026)
by: Kostrzewa, Marcin, et al.
Published: (2026)
Explaining Concept Drift through the Evolution of Group Counterfactuals
by: Stępka, Ignacy, et al.
Published: (2025)
by: Stępka, Ignacy, et al.
Published: (2025)
Are Foundation Models Useful for Bankruptcy Prediction?
by: Kostrzewa, Marcin, et al.
Published: (2025)
by: Kostrzewa, Marcin, et al.
Published: (2025)
The Problem of Coherence in Natural Language Explanations of Recommendations
by: Raczyński, Jakub, et al.
Published: (2023)
by: Raczyński, Jakub, et al.
Published: (2023)
Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
by: Marzari, Luca, et al.
Published: (2024)
by: Marzari, Luca, et al.
Published: (2024)
A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
by: Stępka, Ignacy, et al.
Published: (2024)
by: Stępka, Ignacy, et al.
Published: (2024)
Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations
by: Karolczak, Jacek, et al.
Published: (2026)
by: Karolczak, Jacek, et al.
Published: (2026)
Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
by: Hamman, Faisal, et al.
Published: (2023)
by: Hamman, Faisal, et al.
Published: (2023)
Interval Abstractions for Robust Counterfactual Explanations
by: Jiang, Junqi, et al.
Published: (2024)
by: Jiang, Junqi, et al.
Published: (2024)
A-PETE: Adaptive Prototype Explanations of Tree Ensembles
by: Karolczak, Jacek, et al.
Published: (2024)
by: Karolczak, Jacek, et al.
Published: (2024)
RobustX: Robust Counterfactual Explanations Made Easy
by: Jiang, Junqi, et al.
Published: (2025)
by: Jiang, Junqi, et al.
Published: (2025)
Robust Stochastic Graph Generator for Counterfactual Explanations
by: Prado-Romero, Mario Alfonso, et al.
Published: (2023)
by: Prado-Romero, Mario Alfonso, et al.
Published: (2023)
Robust Counterfactual Explanations in Machine Learning: A Survey
by: Jiang, Junqi, et al.
Published: (2024)
by: Jiang, Junqi, et al.
Published: (2024)
Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps
by: Sztukiewicz, Lukasz, et al.
Published: (2025)
by: Sztukiewicz, Lukasz, et al.
Published: (2025)
Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
by: Wojak-Strzelecka, Natalia, et al.
Published: (2026)
by: Wojak-Strzelecka, Natalia, et al.
Published: (2026)
The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations
by: Christodoulou, Leonidas, et al.
Published: (2026)
by: Christodoulou, Leonidas, et al.
Published: (2026)
Provably Robust Bayesian Counterfactual Explanations under Model Changes
by: Duell, Jamie, et al.
Published: (2026)
by: Duell, Jamie, et al.
Published: (2026)
Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
by: Jiang, Junqi, et al.
Published: (2023)
by: Jiang, Junqi, et al.
Published: (2023)
Generally-Occurring Model Change for Robust Counterfactual Explanations
by: Xu, Ao, et al.
Published: (2024)
by: Xu, Ao, et al.
Published: (2024)
DetoxAI: a Python Toolkit for Debiasing Deep Learning Models in Computer Vision
by: Stępka, Ignacy, et al.
Published: (2025)
by: Stępka, Ignacy, et al.
Published: (2025)
From Prototypes to Sparse ECG Explanations: SHAP-Driven Counterfactuals for Multivariate Time-Series Multi-class Classification
by: Mozolewski, Maciej, et al.
Published: (2025)
by: Mozolewski, Maciej, et al.
Published: (2025)
Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization
by: Kinjo, Keita
Published: (2025)
by: Kinjo, Keita
Published: (2025)
Calibrated Explanations: with Uncertainty Information and Counterfactuals
by: Lofstrom, Helena, et al.
Published: (2023)
by: Lofstrom, Helena, et al.
Published: (2023)
Distributional Counterfactual Explanations With Optimal Transport
by: You, Lei, et al.
Published: (2024)
by: You, Lei, et al.
Published: (2024)
Galaxy Morphology Classification with Counterfactual Explanation
by: Cao, Zhuo, et al.
Published: (2025)
by: Cao, Zhuo, et al.
Published: (2025)
The Effect of Data Poisoning on Counterfactual Explanations
by: Artelt, André, et al.
Published: (2024)
by: Artelt, André, et al.
Published: (2024)
TriShGAN: Enhancing Sparsity and Robustness in Multivariate Time Series Counterfactuals Explanation
by: Ma, Hongnan, et al.
Published: (2025)
by: Ma, Hongnan, et al.
Published: (2025)
Graph Edits for Counterfactual Explanations: A comparative study
by: Dimitriou, Angeliki, et al.
Published: (2024)
by: Dimitriou, Angeliki, et al.
Published: (2024)
On the Hardness of Computing Counterfactual and Semifactual Explanations in XAI
by: Artelt, André, et al.
Published: (2026)
by: Artelt, André, et al.
Published: (2026)
On the Definition and Detection of Cherry-Picking in Counterfactual Explanations
by: Hinns, James, et al.
Published: (2026)
by: Hinns, James, et al.
Published: (2026)
Counterfactual Explanations for Continuous Action Reinforcement Learning
by: Dong, Shuyang, et al.
Published: (2025)
by: Dong, Shuyang, et al.
Published: (2025)
Generating Counterfactual Explanations Using Cardinality Constraints
by: Ruiz-Torrubiano, Rubén
Published: (2024)
by: Ruiz-Torrubiano, Rubén
Published: (2024)
Deep Backtracking Counterfactuals for Causally Compliant Explanations
by: Kladny, Klaus-Rudolf, et al.
Published: (2023)
by: Kladny, Klaus-Rudolf, et al.
Published: (2023)
A New Approach to Backtracking Counterfactual Explanations: A Unified Causal Framework for Efficient Model Interpretability
by: Fatemi, Pouria, et al.
Published: (2025)
by: Fatemi, Pouria, et al.
Published: (2025)
Locally-Minimal Probabilistic Explanations
by: Izza, Yacine, et al.
Published: (2023)
by: Izza, Yacine, et al.
Published: (2023)
Flexible Counterfactual Explanations with Generative Models
by: Hellemans, Stig, et al.
Published: (2025)
by: Hellemans, Stig, et al.
Published: (2025)
Similar Items
-
Counterfactual Explanations Under Concept Drift
by: Kostrzewa, Marcin, et al.
Published: (2026) -
Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
by: Wielopolski, Patryk, et al.
Published: (2024) -
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
by: Stępka, Ignacy, et al.
Published: (2024) -
Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels
by: Furman, Oleksii, et al.
Published: (2024) -
Towards plausibility in time series counterfactual explanations
by: Kostrzewa, Marcin, et al.
Published: (2026)