Cover Image

Counterfactual Explanations Under Concept Drift

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kostrzewa, Marcin, Stefanowski, Jerzy, Zięba, Maciej
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

Explaining Concept Drift through the Evolution of Group Counterfactuals
by: Stępka, Ignacy, et al.
Published: (2025)

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)

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

A-PETE: Adaptive Prototype Explanations of Tree Ensembles
by: Karolczak, Jacek, et al.
Published: (2024)

Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations
by: Karolczak, Jacek, et al.
Published: (2026)

Are Foundation Models Useful for Bankruptcy Prediction?
by: Kostrzewa, Marcin, et al.
Published: (2025)

The Problem of Coherence in Natural Language Explanations of Recommendations
by: Raczyński, Jakub, et al.
Published: (2023)

CounterFlowNet: From Minimal Changes to Meaningful Counterfactual Explanations
by: Furman, Oleksii, et al.
Published: (2026)

An interpretable prototype parts-based neural network for medical tabular data
by: Karolczak, Jacek, et al.
Published: (2026)

This part looks alike this: identifying important parts of explained instances and prototypes
by: Karolczak, Jacek, et al.
Published: (2025)

Improving Online Bagging for Complex Imbalanced Data Stream
by: Przybyl, Bartosz, et al.
Published: (2024)

PREF-XAI: Preference-Based Personalized Rule Explanations of Black-Box Machine Learning Models
by: Greco, Salvatore, et al.
Published: (2026)

V4FinBench: Benchmarking Tabular Foundation Models, LLMs, and Standard Methods on Corporate Bankruptcy Prediction
by: Kostrzewa, Marcin, et al.
Published: (2026)

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)

TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression
by: Wielopolski, Patryk, et al.
Published: (2022)

Causal Explanation of Concept Drift -- A Truly Actionable Approach
by: Komnick, David, et al.
Published: (2025)

Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps
by: Sztukiewicz, Lukasz, et al.
Published: (2025)

DetoxAI: a Python Toolkit for Debiasing Deep Learning Models in Computer Vision
by: Stępka, Ignacy, et al.
Published: (2025)

Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
by: Wojak-Strzelecka, Natalia, et al.
Published: (2026)

Counterfactual Explanations on Robust Perceptual Geodesics
by: Zaher, Eslam, et al.
Published: (2026)

CoLa-DCE -- Concept-guided Latent Diffusion Counterfactual Explanations
by: Motzkus, Franz, et al.
Published: (2024)

Online Detection of Water Contamination Under Concept Drift
by: Li, Jin, et al.
Published: (2025)

Tabular Diffusion Counterfactual Explanations
by: Zhang, Wei, et al.
Published: (2025)

Graph Diffusion Counterfactual Explanation
by: Bechtoldt, David, et al.
Published: (2025)

Fair and Explainable Credit-Scoring under Concept Drift: Adaptive Explanation Frameworks for Evolving Populations
by: John, Shivogo
Published: (2025)

Watermarking Counterfactual Explanations
by: Guo, Hangzhi, et al.
Published: (2024)

Optimal Transport Group Counterfactual Explanations
by: Valero-Leal, Enrique, et al.
Published: (2026)

ACE: Adapting sampling for Counterfactual Explanations
by: Guerrero, Margarita A., et al.
Published: (2025)

Counterfactual Explanations for Linear Optimization
by: Kurtz, Jannis, et al.
Published: (2024)

Plausible Counterfactual Explanations of Recommendations
by: Černý, Jakub, et al.
Published: (2025)

S-CFE: Simple Counterfactual Explanations
by: Sadiku, Shpresim, et al.
Published: (2024)

DISCOVER: A Solver for Distributional Counterfactual Explanations
by: Gu, Yikai, et al.
Published: (2026)

CF-OPT: Counterfactual Explanations for Structured Prediction
by: Vivier-Ardisson, Germain, et al.
Published: (2024)

Motif-guided Time Series Counterfactual Explanations
by: Li, Peiyu, et al.
Published: (2022)

Counterfactual Explanations for k-means and Gaussian Clustering
by: Vardakas, Georgios, et al.
Published: (2025)

One-for-many Counterfactual Explanations by Column Generation
by: Lodi, Andrea, et al.
Published: (2024)

CINNAMON: A hybrid approach to change point detection and parameter estimation in single-particle tracking data
by: Malinowski, Jakub, et al.
Published: (2025)