Beyond single-model XAI: aggregating multi-model explanations for enhanced trustworthiness
Fuente:
arXiv
Saved in:
| Main Authors: | Vascotto, Ilaria, Rodriguez, Alex, Bonaita, Alessandro, Bortolussi, Luca |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Assessing reliability of explanations in unbalanced datasets: a use-case on the occurrence of frost events
by: Vascotto, Ilaria, et al.
Published: (2025)
by: Vascotto, Ilaria, et al.
Published: (2025)
When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances
by: Vascotto, Ilaria, et al.
Published: (2024)
by: Vascotto, Ilaria, et al.
Published: (2024)
Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness
by: Ballarin, Emanuele, et al.
Published: (2023)
by: Ballarin, Emanuele, et al.
Published: (2023)
Retrieval-Augmented Mining of Temporal Logic Specifications from Data
by: Saveri, Gaia, et al.
Published: (2024)
by: Saveri, Gaia, et al.
Published: (2024)
Introducing δ-XAI: a novel sensitivity-based method for local AI explanations
by: De Carlo, Alessandro, et al.
Published: (2024)
by: De Carlo, Alessandro, et al.
Published: (2024)
How can we trust opaque systems? Criteria for robust explanations in XAI
by: Boge, Florian J., et al.
Published: (2025)
by: Boge, Florian J., et al.
Published: (2025)
I-trustworthy Models. A framework for trustworthiness evaluation of probabilistic classifiers
by: Vashistha, Ritwik, et al.
Published: (2025)
by: Vashistha, Ritwik, et al.
Published: (2025)
Timeseria: an object-oriented time series processing library
by: Russo, Stefano Alberto, et al.
Published: (2024)
by: Russo, Stefano Alberto, et al.
Published: (2024)
Graph-Conditional Flow Matching for Relational Data Generation
by: Scassola, Davide, et al.
Published: (2025)
by: Scassola, Davide, et al.
Published: (2025)
Intrinsic Dimension Correlation: uncovering nonlinear connections in multimodal representations
by: Basile, Lorenzo, et al.
Published: (2024)
by: Basile, Lorenzo, et al.
Published: (2024)
Why do explanations fail? A typology and discussion on failures in XAI
by: Bove, Clara, et al.
Published: (2024)
by: Bove, Clara, et al.
Published: (2024)
Beyond saliency: enhancing explanation of speech emotion recognition with expert-referenced acoustic cues
by: Nasr, Seham, et al.
Published: (2025)
by: Nasr, Seham, et al.
Published: (2025)
On GNN explanability with activation rules
by: Veyrin-Forrer, Luca, et al.
Published: (2024)
by: Veyrin-Forrer, Luca, et al.
Published: (2024)
GENEOnet: Statistical analysis supporting explainability and trustworthiness
by: Bocchi, Giovanni, et al.
Published: (2025)
by: Bocchi, Giovanni, et al.
Published: (2025)
Fast ML-driven Analog Circuit Layout using Reinforcement Learning and Steiner Trees
by: Basso, Davide, et al.
Published: (2024)
by: Basso, Davide, et al.
Published: (2024)
Frequency maps reveal the correlation between Adversarial Attacks and Implicit Bias
by: Basile, Lorenzo, et al.
Published: (2023)
by: Basile, Lorenzo, et al.
Published: (2023)
Feature salience - not task-informativeness - drives machine learning model explanations
by: Clark, Benedict, et al.
Published: (2026)
by: Clark, Benedict, et al.
Published: (2026)
Conformal online model aggregation
by: Gasparin, Matteo, et al.
Published: (2024)
by: Gasparin, Matteo, et al.
Published: (2024)
Characterizing the contribution of dependent features in XAI methods
by: Salih, Ahmed, et al.
Published: (2023)
by: Salih, Ahmed, et al.
Published: (2023)
Zero-Shot Conditioning of Score-Based Diffusion Models by Neuro-Symbolic Constraints
by: Scassola, Davide, et al.
Published: (2023)
by: Scassola, Davide, et al.
Published: (2023)
Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptation
by: Verdù, Federico Julian Camerota, et al.
Published: (2024)
by: Verdù, Federico Julian Camerota, et al.
Published: (2024)
ECATS: Explainable-by-design concept-based anomaly detection for time series
by: Ferfoglia, Irene, et al.
Published: (2024)
by: Ferfoglia, Irene, et al.
Published: (2024)
CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models
by: Singh, Abhishek, et al.
Published: (2024)
by: Singh, Abhishek, et al.
Published: (2024)
Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semantics
by: Ferfoglia, Irene, et al.
Published: (2025)
by: Ferfoglia, Irene, et al.
Published: (2025)
Towards Interpretable Concept Learning over Time Series via Temporal Logic Semantics
by: Ferfoglia, Irene, et al.
Published: (2025)
by: Ferfoglia, Irene, et al.
Published: (2025)
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations
by: Swamy, Vinitra, et al.
Published: (2023)
by: Swamy, Vinitra, et al.
Published: (2023)
Evaluating the stability of model explanations in instance-dependent cost-sensitive credit scoring
by: Ballegeer, Matteo, et al.
Published: (2025)
by: Ballegeer, Matteo, et al.
Published: (2025)
An effect analysis of the balancing techniques on the counterfactual explanations of student success prediction models
by: Cavus, Mustafa, et al.
Published: (2024)
by: Cavus, Mustafa, et al.
Published: (2024)
Explainable bank failure prediction models: Counterfactual explanations to reduce the failure risk
by: Gunonu, Seyma, et al.
Published: (2024)
by: Gunonu, Seyma, et al.
Published: (2024)
EXACT: Towards a platform for empirically benchmarking Machine Learning model explanation methods
by: Clark, Benedict, et al.
Published: (2024)
by: Clark, Benedict, et al.
Published: (2024)
Advancing Routing-Awareness in Analog ICs Floorplanning
by: Basso, Davide, et al.
Published: (2025)
by: Basso, Davide, et al.
Published: (2025)
Bayesian meta learning for trustworthy uncertainty quantification
by: Yuan, Zhenyuan, et al.
Published: (2024)
by: Yuan, Zhenyuan, et al.
Published: (2024)
CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests
by: Dandl, Susanne, et al.
Published: (2024)
by: Dandl, Susanne, et al.
Published: (2024)
CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Series
by: Pearson, Nicholas A., et al.
Published: (2025)
by: Pearson, Nicholas A., et al.
Published: (2025)
IMPACTX: improving model performance by appropriately constraining the training with teacher explanations
by: Apicella, Andrea, et al.
Published: (2025)
by: Apicella, Andrea, et al.
Published: (2025)
A multi-model approach using XAI and anomaly detection to predict asteroid hazards
by: Mondal, Amit Kumar, et al.
Published: (2025)
by: Mondal, Amit Kumar, et al.
Published: (2025)
Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?
by: Pu, Yingming, et al.
Published: (2024)
by: Pu, Yingming, et al.
Published: (2024)
Explaining deep neural network models for electricity price forecasting with XAI
by: Pesenti, Antoine, et al.
Published: (2025)
by: Pesenti, Antoine, et al.
Published: (2025)
Applying the maximum entropy principle to neural networks enhances multi-species distribution models
by: Ryckewaert, Maxime, et al.
Published: (2024)
by: Ryckewaert, Maxime, et al.
Published: (2024)
Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic
by: Bonin, Lorenzo, et al.
Published: (2026)
by: Bonin, Lorenzo, et al.
Published: (2026)
Similar Items
-
Assessing reliability of explanations in unbalanced datasets: a use-case on the occurrence of frost events
by: Vascotto, Ilaria, et al.
Published: (2025) -
When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances
by: Vascotto, Ilaria, et al.
Published: (2024) -
Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness
by: Ballarin, Emanuele, et al.
Published: (2023) -
Retrieval-Augmented Mining of Temporal Logic Specifications from Data
by: Saveri, Gaia, et al.
Published: (2024) -
Introducing δ-XAI: a novel sensitivity-based method for local AI explanations
by: De Carlo, Alessandro, et al.
Published: (2024)