Explaining Concept Drift through the Evolution of Group Counterfactuals
Fuente:
arXiv
Saved in:
| Main Authors: | Stępka, Ignacy, Stefanowski, Jerzy |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
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)
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)
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)
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)
Counterfactual Explanations Under Concept Drift
by: Kostrzewa, Marcin, et al.
Published: (2026)
by: Kostrzewa, Marcin, et al.
Published: (2026)
A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
by: Kostrzewa, Marcin, et al.
Published: (2026)
by: Kostrzewa, Marcin, et al.
Published: (2026)
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)
Probabilistically Plausible Counterfactual Explanations with Normalizing Flows
by: Wielopolski, Patryk, et al.
Published: (2024)
by: Wielopolski, Patryk, et al.
Published: (2024)
Mitigating Persistent Client Dropout in Asynchronous Decentralized Federated Learning
by: Stępka, Ignacy, et al.
Published: (2025)
by: Stępka, Ignacy, 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)
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)
Explaining Drift using Shapley Values
by: Edakunni, Narayanan U., et al.
Published: (2024)
by: Edakunni, Narayanan U., et al.
Published: (2024)
Explaining Learned Reward Functions with Counterfactual Trajectories
by: Wehner, Jan, et al.
Published: (2024)
by: Wehner, Jan, et al.
Published: (2024)
Counterfactual Concept Bottleneck Models
by: Dominici, Gabriele, et al.
Published: (2024)
by: Dominici, Gabriele, et al.
Published: (2024)
Explaining k-Nearest Neighbors: Abductive and Counterfactual Explanations
by: Barceló, Pablo, et al.
Published: (2025)
by: Barceló, Pablo, et al.
Published: (2025)
A SAT-based approach to rigorous verification of Bayesian networks
by: Stępka, Ignacy, et al.
Published: (2024)
by: Stępka, Ignacy, et al.
Published: (2024)
ACTER: Diverse and Actionable Counterfactual Sequences for Explaining and Diagnosing RL Policies
by: Gajcin, Jasmina, et al.
Published: (2024)
by: Gajcin, Jasmina, et al.
Published: (2024)
Online Drift Detection with Maximum Concept Discrepancy
by: Wan, Ke, et al.
Published: (2024)
by: Wan, Ke, et al.
Published: (2024)
Lyapunov-Stable Adaptive Control for Multimodal Concept Drift
by: Pan, Tianyu Bell, et al.
Published: (2025)
by: Pan, Tianyu Bell, et al.
Published: (2025)
Online Detection of Water Contamination Under Concept Drift
by: Li, Jin, et al.
Published: (2025)
by: Li, Jin, et al.
Published: (2025)
Explaining Fine Tuned LLMs via Counterfactuals A Knowledge Graph Driven Framework
by: Wang, Yucheng, et al.
Published: (2025)
by: Wang, Yucheng, et al.
Published: (2025)
Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery
by: Varshney, Payal, et al.
Published: (2024)
by: Varshney, Payal, et al.
Published: (2024)
Conceptualizing Uncertainty: A Concept-based Approach to Explaining Uncertainty
by: Roberts, Isaac, et al.
Published: (2025)
by: Roberts, Isaac, et al.
Published: (2025)
Explaining Robustness to Catastrophic Forgetting Through Incremental Concept Formation
by: Barari, Nicki, et al.
Published: (2025)
by: Barari, Nicki, et al.
Published: (2025)
This part looks alike this: identifying important parts of explained instances and prototypes
by: Karolczak, Jacek, et al.
Published: (2025)
by: Karolczak, Jacek, et al.
Published: (2025)
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)
An interpretable prototype parts-based neural network for medical tabular data
by: Karolczak, Jacek, et al.
Published: (2026)
by: Karolczak, Jacek, et al.
Published: (2026)
A-PETE: Adaptive Prototype Explanations of Tree Ensembles
by: Karolczak, Jacek, et al.
Published: (2024)
by: Karolczak, Jacek, et al.
Published: (2024)
Improving Online Bagging for Complex Imbalanced Data Stream
by: Przybyl, Bartosz, et al.
Published: (2024)
by: Przybyl, Bartosz, et al.
Published: (2024)
CORAL: Concept Drift Representation Learning for Co-evolving Time-series
by: Xu, Kunpeng, et al.
Published: (2025)
by: Xu, Kunpeng, et al.
Published: (2025)
Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection
by: Zhang, Yiqun, et al.
Published: (2026)
by: Zhang, Yiqun, et al.
Published: (2026)
Online Boosting Adaptive Learning under Concept Drift for Multistream Classification
by: Yu, En, et al.
Published: (2023)
by: Yu, En, et al.
Published: (2023)
FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments
by: Mavromatis, Ioannis, et al.
Published: (2024)
by: Mavromatis, Ioannis, et al.
Published: (2024)
UniCoMTE: A Universal Counterfactual Framework for Explaining Time-Series Classifiers on ECG Data
by: Li, Justin, et al.
Published: (2025)
by: Li, Justin, et al.
Published: (2025)
Generalized Incremental Learning under Concept Drift across Evolving Data Streams
by: Yu, En, et al.
Published: (2025)
by: Yu, En, et al.
Published: (2025)
WormKAN: Are KAN Effective for Identifying and Tracking Concept Drift in Time Series?
by: Xu, Kunpeng, et al.
Published: (2024)
by: Xu, Kunpeng, et al.
Published: (2024)
Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time
by: Greco, Salvatore, et al.
Published: (2024)
by: Greco, Salvatore, et al.
Published: (2024)
Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanations
by: Ezzeddine, Fatima, et al.
Published: (2026)
by: Ezzeddine, Fatima, et al.
Published: (2026)
FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness
by: Fragkathoulas, Christos, et al.
Published: (2024)
by: Fragkathoulas, Christos, et al.
Published: (2024)
CoLa-DCE -- Concept-guided Latent Diffusion Counterfactual Explanations
by: Motzkus, Franz, et al.
Published: (2024)
by: Motzkus, Franz, et al.
Published: (2024)
Similar Items
-
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
by: Stępka, Ignacy, 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) -
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) -
Counterfactual Explanations Under Concept Drift
by: Kostrzewa, Marcin, et al.
Published: (2026)