:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Liao, Zhan-Yi, Yoo, Jaewon, Yang, Hao-Tsung, Chen, Po-An
Format:	Preprint
Published:	2026
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2602.08021
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

From What Ifs to Insights: Counterfactuals in Causal Inference vs. Explainable AI
by: Shmueli, Galit, et al.
Published: (2025)

Comparative Explanations via Counterfactual Reasoning in Recommendations
by: Yu, Yi, et al.
Published: (2025)

A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers
by: McAleese, Stephen, et al.
Published: (2024)

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)

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

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)

Ranking Counterfactual Explanations
by: Lim, Suryani, et al.
Published: (2025)

The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations
by: Christodoulou, Leonidas, et al.
Published: (2026)

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

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

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

Generally-Occurring Model Change for Robust Counterfactual Explanations
by: Xu, Ao, 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)

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

Counterfactual Explanations as Plans
by: Belle, Vaishak
Published: (2025)

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

DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual Explanations
by: Augustin, Maximilian, et al.
Published: (2023)

LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching
by: Cao, Zhuo, et al.
Published: (2025)

Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
by: Chu, Zhaoyang, et al.
Published: (2024)

Procedural Fairness via Group Counterfactual Explanation
by: Popoola, Gideon, et al.
Published: (2026)

Causal Explanations for Image Classifiers
by: Chockler, Hana, et al.
Published: (2024)

Counterfactual Simulatability of LLM Explanations for Generation Tasks
by: Limpijankit, Marvin, et al.
Published: (2025)

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

Evaluating Counterfactual Explanation Methods on Incomplete Inputs
by: Leofante, Francesco, et al.
Published: (2026)

Generating Counterfactual Explanations Under Temporal Constraints
by: Buliga, Andrei, et al.
Published: (2025)

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

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)

Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection
by: Galwaduge, Vinura, et al.
Published: (2025)

Provable Robust Saliency-based Explanations
by: Chen, Chao, et al.
Published: (2022)

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

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

Generating Causally Compliant Counterfactual Explanations using ASP
by: Dasgupta, Sopam
Published: (2025)

ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks
by: Zhang, Yu, et al.
Published: (2026)

V-CECE: Visual Counterfactual Explanations via Conceptual Edits
by: Spanos, Nikolaos, et al.
Published: (2025)