Viewing the process of generating counterfactuals as a source of knowledge: a new approach for explaining classifiers
Fuente:
arXiv
Saved in:
| Main Authors: | Lemaire, Vincent, Boudec, Nathan Le, Guyomard, Victor, Fessant, Françoise |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Global explainability of a deep abstaining classifier
by: Dhaubhadel, Sayera, et al.
Published: (2025)
by: Dhaubhadel, Sayera, et al.
Published: (2025)
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)
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
by: Buliga, Andrei, et al.
Published: (2024)
by: Buliga, Andrei, et al.
Published: (2024)
HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data
by: Marszałek, Patryk, et al.
Published: (2025)
by: Marszałek, Patryk, et al.
Published: (2025)
A new Input Convex Neural Network with application to options pricing
by: Lemaire, Vincent, et al.
Published: (2024)
by: Lemaire, Vincent, et al.
Published: (2024)
An analysis of the noise schedule for score-based generative models
by: Strasman, Stanislas, et al.
Published: (2024)
by: Strasman, Stanislas, et al.
Published: (2024)
Breaking SafetyCore: Exploring the Risks of On-Device AI Deployment
by: Guyomard, Victor, et al.
Published: (2025)
by: Guyomard, Victor, et al.
Published: (2025)
Constructing Variables Using Classifiers as an Aid to Regression: An Empirical Assessment
by: Troisemaine, Colin, et al.
Published: (2024)
by: Troisemaine, Colin, et al.
Published: (2024)
DEMAU: Decompose, Explore, Model and Analyse Uncertainties
by: Hoarau, Arthur, et al.
Published: (2024)
by: Hoarau, Arthur, et al.
Published: (2024)
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanations
by: Bobek, Szymon, et al.
Published: (2023)
by: Bobek, Szymon, et al.
Published: (2023)
Efficient Generative Transformer Operators For Million-Point PDEs
by: Koupaï, Armand Kassaï, et al.
Published: (2025)
by: Koupaï, Armand Kassaï, et al.
Published: (2025)
Actionable and diverse counterfactual explanations incorporating domain knowledge and plausibility constraints
by: Bobek, Szymon, et al.
Published: (2025)
by: Bobek, Szymon, et al.
Published: (2025)
Temporal Sepsis Modeling: a Relational and Explainable-by-Design Framework
by: Lemaire, Vincent, et al.
Published: (2026)
by: Lemaire, Vincent, et al.
Published: (2026)
ENMA: Tokenwise Autoregression for Generative Neural PDE Operators
by: Koupaï, Armand Kassaï, et al.
Published: (2025)
by: Koupaï, Armand Kassaï, et al.
Published: (2025)
Simulating counterfactuals
by: Karvanen, Juha, et al.
Published: (2023)
by: Karvanen, Juha, et al.
Published: (2023)
On Forgetting and Stability of Score-based Generative models
by: Strasman, Stanislas, et al.
Published: (2026)
by: Strasman, Stanislas, et al.
Published: (2026)
PolyGraph Discrepancy: a classifier-based metric for graph generation
by: Krimmel, Markus, et al.
Published: (2025)
by: Krimmel, Markus, et al.
Published: (2025)
InFusionLayer: a CFA-based ensemble tool to generate new classifiers for learning and modeling
by: Roginek, Eric, et al.
Published: (2026)
by: Roginek, Eric, et al.
Published: (2026)
Evidential uncertainty sampling for active learning
by: Hoarau, Arthur, et al.
Published: (2023)
by: Hoarau, Arthur, et al.
Published: (2023)
Early Classification of Time Series: A Survey and Benchmark
by: Renault, Aurélien, et al.
Published: (2024)
by: Renault, Aurélien, et al.
Published: (2024)
Calibration improves detection of mislabeled examples
by: Chibane, Ilies, et al.
Published: (2025)
by: Chibane, Ilies, et al.
Published: (2025)
Early Classification of Time Series in Non-Stationary Cost Regimes
by: Renault, Aurélien, et al.
Published: (2026)
by: Renault, Aurélien, et al.
Published: (2026)
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark
by: George, Thomas, et al.
Published: (2024)
by: George, Thomas, et al.
Published: (2024)
Deep Reinforcement Learning based Triggering Function for Early Classifiers of Time Series
by: Renault, Aurélien, et al.
Published: (2025)
by: Renault, Aurélien, et al.
Published: (2025)
SCE-LITE-HQ: Smooth visual counterfactual explanations with generative foundation models
by: Zeid, Ahmed, et al.
Published: (2026)
by: Zeid, Ahmed, et al.
Published: (2026)
Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods
by: Boudec, Lise Le, et al.
Published: (2024)
by: Boudec, Lise Le, et al.
Published: (2024)
MCCE: Monte Carlo sampling of realistic counterfactual explanations
by: Redelmeier, Annabelle, et al.
Published: (2021)
by: Redelmeier, Annabelle, et al.
Published: (2021)
Soft Decision Tree classifier: explainable and extendable PyTorch implementation
by: Shamir, Reuben R
Published: (2025)
by: Shamir, Reuben R
Published: (2025)
Robustness quantification: a new method for assessing the reliability of the predictions of a classifier
by: Detavernier, Adrián, et al.
Published: (2025)
by: Detavernier, Adrián, et al.
Published: (2025)
Novel RL approach for efficient Elevator Group Control Systems
by: Vaartjes, Nathan, et al.
Published: (2025)
by: Vaartjes, Nathan, et al.
Published: (2025)
Uncertainty quantification in neural network classifiers -- a local linear approach
by: Malmström, Magnus, et al.
Published: (2023)
by: Malmström, Magnus, et al.
Published: (2023)
Towards plausibility in time series counterfactual explanations
by: Kostrzewa, Marcin, et al.
Published: (2026)
by: Kostrzewa, Marcin, et al.
Published: (2026)
Wasserstein Convergence of Critically Damped Langevin Diffusions
by: Strasman, Stanislas, et al.
Published: (2025)
by: Strasman, Stanislas, et al.
Published: (2025)
Intriguing properties of generative classifiers
by: Jaini, Priyank, et al.
Published: (2023)
by: Jaini, Priyank, et al.
Published: (2023)
A 3D deep learning classifier and its explainability when assessing coronary artery disease
by: Cheung, Wing Keung, et al.
Published: (2023)
by: Cheung, Wing Keung, et al.
Published: (2023)
Machine learning models for estimating counterfactuals in a single-arm inflammatory bowel disease study
by: Liu, Dan, et al.
Published: (2026)
by: Liu, Dan, et al.
Published: (2026)
A Practical Approach to Novel Class Discovery in Tabular Data
by: Troisemaine, Colin, et al.
Published: (2023)
by: Troisemaine, Colin, et al.
Published: (2023)
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control
by: Furman, Oleksii, et al.
Published: (2025)
by: Furman, Oleksii, et al.
Published: (2025)
Towards counterfactual fairness through auxiliary variables
by: Tian, Bowei, et al.
Published: (2024)
by: Tian, Bowei, et al.
Published: (2024)
On Predictive planning and counterfactual learning in active inference
by: Paul, Aswin, et al.
Published: (2024)
by: Paul, Aswin, et al.
Published: (2024)
Similar Items
-
Global explainability of a deep abstaining classifier
by: Dhaubhadel, Sayera, et al.
Published: (2025) -
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) -
Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring
by: Buliga, Andrei, et al.
Published: (2024) -
HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data
by: Marszałek, Patryk, et al.
Published: (2025) -
A new Input Convex Neural Network with application to options pricing
by: Lemaire, Vincent, et al.
Published: (2024)