Saved in:
| Main Authors: | Paterakis, George, Castellani, Andrea, Papoutsoglou, George, Rodemann, Tobias, Tsamardinos, Ioannis |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.11529 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection
by: Castellani, Andrea, et al.
Published: (2025)
by: Castellani, Andrea, et al.
Published: (2025)
Confidence Interval Estimation of Predictive Performance in the Context of AutoML
by: Paraschakis, Konstantinos, et al.
Published: (2024)
by: Paraschakis, Konstantinos, et al.
Published: (2024)
A Comparative Analysis of Influence Signals for Data Debugging
by: Myrtakis, Nikolaos, et al.
Published: (2025)
by: Myrtakis, Nikolaos, et al.
Published: (2025)
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML
by: Borboudakis, Giorgos, et al.
Published: (2023)
by: Borboudakis, Giorgos, et al.
Published: (2023)
Towards Bayesian Data Selection
by: Rodemann, Julian
Published: (2024)
by: Rodemann, Julian
Published: (2024)
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
by: Krishna, Satyapriya, et al.
Published: (2022)
by: Krishna, Satyapriya, et al.
Published: (2022)
Agentopic: A Generative AI Agent Workflow for Explainable Topic Modeling
by: Kok-Shun, Brice Valentin, et al.
Published: (2026)
by: Kok-Shun, Brice Valentin, et al.
Published: (2026)
Verifying Machine Unlearning with Explainable AI
by: Vidal, Àlex Pujol, et al.
Published: (2024)
by: Vidal, Àlex Pujol, et al.
Published: (2024)
Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets
by: Vourganas, Ioannis J., et al.
Published: (2026)
by: Vourganas, Ioannis J., et al.
Published: (2026)
A Comprehensive Guide to Explainable AI: From Classical Models to LLMs
by: Hsieh, Weiche, et al.
Published: (2024)
by: Hsieh, Weiche, et al.
Published: (2024)
Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI
by: Paraschou, Eva, et al.
Published: (2025)
by: Paraschou, Eva, et al.
Published: (2025)
AISSISTANT: Human-AI Collaborative Review and Perspective Research Workflows in Data Science
by: Gaddipati, Sasi Kiran, et al.
Published: (2025)
by: Gaddipati, Sasi Kiran, et al.
Published: (2025)
Interpretable and Explainable Surrogate Modeling for Simulations: A State-of-the-Art Survey and Perspectives on Explainable AI for Decision-Making
by: Palar, Pramudita Satria, et al.
Published: (2026)
by: Palar, Pramudita Satria, et al.
Published: (2026)
A Comprehensive Machine Learning Framework for Heart Disease Prediction: Performance Evaluation and Future Perspectives
by: Lamir, Ali Azimi, et al.
Published: (2025)
by: Lamir, Ali Azimi, et al.
Published: (2025)
Exploring Machine Learning, Deep Learning, and Explainable AI Methods for Seasonal Precipitation Prediction in South America
by: Domingos, Matheus Corrêa, et al.
Published: (2025)
by: Domingos, Matheus Corrêa, et al.
Published: (2025)
Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
by: Rodemann, Julian, et al.
Published: (2026)
by: Rodemann, Julian, et al.
Published: (2026)
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space
by: Pitsiorlas, Ioannis, et al.
Published: (2024)
by: Pitsiorlas, Ioannis, et al.
Published: (2024)
Reciprocal Learning
by: Rodemann, Julian, et al.
Published: (2024)
by: Rodemann, Julian, et al.
Published: (2024)
A Statistical Case Against Empirical Human-AI Alignment
by: Rodemann, Julian, et al.
Published: (2025)
by: Rodemann, Julian, et al.
Published: (2025)
Investigating the Duality of Interpretability and Explainability in Machine Learning
by: Garouani, Moncef, et al.
Published: (2025)
by: Garouani, Moncef, et al.
Published: (2025)
On the Relationship Between Interpretability and Explainability in Machine Learning
by: Leblanc, Benjamin, et al.
Published: (2023)
by: Leblanc, Benjamin, et al.
Published: (2023)
Explainable Machine Learning for ICU Readmission Prediction
by: de Sá, Alex G. C., et al.
Published: (2023)
by: de Sá, Alex G. C., et al.
Published: (2023)
Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey
by: Gu, Yang, et al.
Published: (2024)
by: Gu, Yang, et al.
Published: (2024)
Couler: Unified Machine Learning Workflow Optimization in Cloud
by: Wang, Xiaoda, et al.
Published: (2024)
by: Wang, Xiaoda, et al.
Published: (2024)
Does Faithfulness Conflict with Plausibility? An Empirical Study in Explainable AI across NLP Tasks
by: Lu, Xiaolei, et al.
Published: (2024)
by: Lu, Xiaolei, et al.
Published: (2024)
A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data
by: Pitsiorlas, Ioannis, et al.
Published: (2024)
by: Pitsiorlas, Ioannis, et al.
Published: (2024)
DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks
by: Jiang, Qile, et al.
Published: (2025)
by: Jiang, Qile, et al.
Published: (2025)
Performative Learning Theory
by: Rodemann, Julian, et al.
Published: (2026)
by: Rodemann, Julian, et al.
Published: (2026)
An evaluation framework for synthetic data generation models
by: Livieris, Ioannis E., et al.
Published: (2024)
by: Livieris, Ioannis E., et al.
Published: (2024)
Analyzing the Impact of Adversarial Examples on Explainable Machine Learning
by: Devabhakthini, Prathyusha, et al.
Published: (2023)
by: Devabhakthini, Prathyusha, et al.
Published: (2023)
Explainability of Machine Learning Models under Missing Data
by: Vo, Tuan L., et al.
Published: (2024)
by: Vo, Tuan L., et al.
Published: (2024)
Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey
by: Rashid, Abdur, et al.
Published: (2024)
by: Rashid, Abdur, et al.
Published: (2024)
Explainable AI for Correct Root Cause Analysis of Product Quality in Injection Moulding
by: Muaz, Muhammad, et al.
Published: (2025)
by: Muaz, Muhammad, et al.
Published: (2025)
Toward Generalizable Graph Learning for 3D Engineering AI: Explainable Workflows for CAE Mode Shape Classification and CFD Field Prediction
by: Son, Tong Duy, et al.
Published: (2026)
by: Son, Tong Duy, et al.
Published: (2026)
Towards Trustworthy Keylogger detection: A Comprehensive Analysis of Ensemble Techniques and Feature Selections through Explainable AI
by: Mahmud, Monirul Islam
Published: (2025)
by: Mahmud, Monirul Islam
Published: (2025)
Explainable Machine Learning Framework for Cardiovascular Disease Diagnosis and Prognosis
by: Sourov, Md. Emon Akter, et al.
Published: (2025)
by: Sourov, Md. Emon Akter, et al.
Published: (2025)
An Explainable Machine Learning Framework for the Accurate Diagnosis of Ovarian Cancer
by: Newaz, Asif, et al.
Published: (2023)
by: Newaz, Asif, et al.
Published: (2023)
Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models
by: Noorani, Sima, et al.
Published: (2025)
by: Noorani, Sima, et al.
Published: (2025)
xEEGNet: Towards Explainable AI in EEG Dementia Classification
by: Zanola, Andrea, et al.
Published: (2025)
by: Zanola, Andrea, et al.
Published: (2025)
Comparing Prior and Learned Time Representations in Transformer Models of Timeseries
by: Koliou, Natalia, et al.
Published: (2024)
by: Koliou, Natalia, et al.
Published: (2024)
Similar Items
-
Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection
by: Castellani, Andrea, et al.
Published: (2025) -
Confidence Interval Estimation of Predictive Performance in the Context of AutoML
by: Paraschakis, Konstantinos, et al.
Published: (2024) -
A Comparative Analysis of Influence Signals for Data Debugging
by: Myrtakis, Nikolaos, et al.
Published: (2025) -
A Meta-Level Learning Algorithm for Sequential Hyper-Parameter Space Reduction in AutoML
by: Borboudakis, Giorgos, et al.
Published: (2023) -
Towards Bayesian Data Selection
by: Rodemann, Julian
Published: (2024)