Cover Image

The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Christodoulou, Leonidas, Sun, Chang
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robust Counterfactual Explanations in Machine Learning: A Survey
by: Jiang, Junqi, et al.
Published: (2024)

Calibrated Explanations: with Uncertainty Information and Counterfactuals
by: Lofstrom, Helena, et al.
Published: (2023)

Interval Abstractions for Robust Counterfactual Explanations
by: Jiang, Junqi, et al.
Published: (2024)

RobustX: Robust Counterfactual Explanations Made Easy
by: Jiang, Junqi, et al.
Published: (2025)

Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
by: Marzari, Luca, et al.
Published: (2024)

Robust Stochastic Graph Generator for Counterfactual Explanations
by: Prado-Romero, Mario Alfonso, et al.
Published: (2023)

Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
by: Stępka, Ignacy, et al.
Published: (2024)

QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
by: Duell, Jamie, et al.
Published: (2024)

Provably Robust Bayesian Counterfactual Explanations under Model Changes
by: Duell, Jamie, et al.
Published: (2026)

A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
by: Kostrzewa, Marcin, et al.
Published: (2026)

TriShGAN: Enhancing Sparsity and Robustness in Multivariate Time Series Counterfactuals Explanation
by: Ma, Hongnan, et al.
Published: (2025)

Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
by: Jiang, Junqi, et al.
Published: (2023)

Counterfactual Explanations for Continuous Action Reinforcement Learning
by: Dong, Shuyang, et al.
Published: (2025)

Generally-Occurring Model Change for Robust Counterfactual Explanations
by: Xu, Ao, et al.
Published: (2024)

Counterfactual Explanations for Deep Learning-Based Traffic Forecasting
by: Wang, Rushan, et al.
Published: (2024)

Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization
by: Kinjo, Keita
Published: (2025)

Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models
by: Löfström, Tuwe, et al.
Published: (2024)

Distributional Counterfactual Explanations With Optimal Transport
by: You, Lei, et al.
Published: (2024)

Galaxy Morphology Classification with Counterfactual Explanation
by: Cao, Zhuo, et al.
Published: (2025)

The Effect of Data Poisoning on Counterfactual Explanations
by: Artelt, André, et al.
Published: (2024)

Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations
by: Sokol, Kacper, et al.
Published: (2025)

UNR-Explainer: Counterfactual Explanations for Unsupervised Node Representation Learning Models
by: Kang, Hyunju, et al.
Published: (2026)

On the Hardness of Computing Counterfactual and Semifactual Explanations in XAI
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)

Generating Counterfactual Explanations Using Cardinality Constraints
by: Ruiz-Torrubiano, Rubén
Published: (2024)

Deep Backtracking Counterfactuals for Causally Compliant Explanations
by: Kladny, Klaus-Rudolf, et al.
Published: (2023)

Flexible Counterfactual Explanations with Generative Models
by: Hellemans, Stig, et al.
Published: (2025)

A Novel Framework for Uncertainty-Driven Adaptive Exploration
by: Bakopoulos, Leonidas, et al.
Published: (2025)

Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees
by: Hamman, Faisal, et al.
Published: (2023)

Counterfactual Training: Teaching Models Plausible and Actionable Explanations
by: Altmeyer, Patrick, et al.
Published: (2026)

FLEX: Feature Importance from Layered Counterfactual Explanations
by: Keshtmand, Nawid, et al.
Published: (2025)

Game-theoretic Counterfactual Explanation for Graph Neural Networks
by: Chhablani, Chirag, et al.
Published: (2024)

Graph Edits for Counterfactual Explanations: A comparative study
by: Dimitriou, Angeliki, et al.
Published: (2024)

Explaining k-Nearest Neighbors: Abductive and Counterfactual Explanations
by: Barceló, Pablo, et al.
Published: (2025)

DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning
by: Bakir, Volkan, et al.
Published: (2025)

Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
by: Wielopolski, Patryk, et al.
Published: (2024)

What-If Explanations Over Time: Counterfactuals for Time Series Classification
by: Schlegel, Udo, et al.
Published: (2026)

Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence
by: Domnich, Marharyta, et al.
Published: (2024)

Counterfactual Explanations for Hypergraph Neural Networks
by: Veglianti, Fabiano, et al.
Published: (2026)

Counterfactual Explanations for Clustering Models
by: Spagnol, Aurora, et al.
Published: (2024)