Guardado en:
| Autores principales: | Catarino, Andre, Melo, Rui, Abreu, Rui, Cruz, Luis |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2506.11026 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Are Sparse Autoencoders Useful for Java Function Bug Detection?
por: Melo, Rui, et al.
Publicado: (2025)
por: Melo, Rui, et al.
Publicado: (2025)
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
por: Zhou, Yan, et al.
Publicado: (2025)
por: Zhou, Yan, et al.
Publicado: (2025)
Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data
por: Hyrup, Tobias, et al.
Publicado: (2023)
por: Hyrup, Tobias, et al.
Publicado: (2023)
Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
por: Nafis, Nazia, et al.
Publicado: (2025)
por: Nafis, Nazia, et al.
Publicado: (2025)
Achievable Fairness on Your Data With Utility Guarantees
por: Taufiq, Muhammad Faaiz, et al.
Publicado: (2024)
por: Taufiq, Muhammad Faaiz, et al.
Publicado: (2024)
The Synthetic Mirror -- Synthetic Data at the Age of Agentic AI
por: Momha, Marcelle
Publicado: (2025)
por: Momha, Marcelle
Publicado: (2025)
Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentation
por: Kulynych, Bogdan, et al.
Publicado: (2026)
por: Kulynych, Bogdan, et al.
Publicado: (2026)
Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective
por: Ganev, Georgi, et al.
Publicado: (2026)
por: Ganev, Georgi, et al.
Publicado: (2026)
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
por: Corbucci, Luca, et al.
Publicado: (2024)
por: Corbucci, Luca, et al.
Publicado: (2024)
Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors
por: Ding, Xueying, et al.
Publicado: (2024)
por: Ding, Xueying, et al.
Publicado: (2024)
Computation-Utility-Privacy Tradeoffs in Bayesian Estimation
por: Chen, Sitan, et al.
Publicado: (2026)
por: Chen, Sitan, et al.
Publicado: (2026)
An Extensive and Methodical Review of Smart Grids for Sustainable Energy Management-Addressing Challenges with AI, Renewable Energy Integration and Leading-edge Technologies
por: Biswas, Parag, et al.
Publicado: (2025)
por: Biswas, Parag, et al.
Publicado: (2025)
Unprocessing Seven Years of Algorithmic Fairness
por: Cruz, André F., et al.
Publicado: (2023)
por: Cruz, André F., et al.
Publicado: (2023)
Quality-Diversity Generative Sampling for Learning with Synthetic Data
por: Chang, Allen, et al.
Publicado: (2023)
por: Chang, Allen, et al.
Publicado: (2023)
SHAP Distance: An Explainability-Aware Metric for Evaluating the Semantic Fidelity of Synthetic Tabular Data
por: Yu, Ke, et al.
Publicado: (2025)
por: Yu, Ke, et al.
Publicado: (2025)
Privacy-Preserved Taxi Demand Prediction System Utilizing Distributed Data
por: Ozeki, Ren, et al.
Publicado: (2024)
por: Ozeki, Ren, et al.
Publicado: (2024)
Training Fair Models in Federated Learning without Data Privacy Infringement
por: Che, Xin, et al.
Publicado: (2021)
por: Che, Xin, et al.
Publicado: (2021)
Opportunities and Challenges of Frontier Data Governance With Synthetic Data
por: Thakur, Madhavendra, et al.
Publicado: (2025)
por: Thakur, Madhavendra, et al.
Publicado: (2025)
Data vs. Model Machine Learning Fairness Testing: An Empirical Study
por: Shome, Arumoy, et al.
Publicado: (2024)
por: Shome, Arumoy, et al.
Publicado: (2024)
Synthetic Data and the Shifting Ground of Truth
por: Offenhuber, Dietmar
Publicado: (2025)
por: Offenhuber, Dietmar
Publicado: (2025)
Unfair Utilities and First Steps Towards Improving Them
por: Jørgensen, Frederik Hytting, et al.
Publicado: (2023)
por: Jørgensen, Frederik Hytting, et al.
Publicado: (2023)
Designing Reputation Systems for Manufacturing Data Trading Markets: A Multi-Agent Evaluation with Q-Learning and IRL-Estimated Utilities
por: Yamamoto, Kenta, et al.
Publicado: (2025)
por: Yamamoto, Kenta, et al.
Publicado: (2025)
TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints
por: Kumar, Vagish, et al.
Publicado: (2026)
por: Kumar, Vagish, et al.
Publicado: (2026)
Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
por: Wu, Yixin, et al.
Publicado: (2025)
por: Wu, Yixin, et al.
Publicado: (2025)
A Unified View of Group Fairness Tradeoffs Using Partial Information Decomposition
por: Hamman, Faisal, et al.
Publicado: (2024)
por: Hamman, Faisal, et al.
Publicado: (2024)
Divide-Conquer Transformer Learning for Predicting Electric Vehicle Charging Events Using Smart Meter Data
por: Ke, Fucai, et al.
Publicado: (2024)
por: Ke, Fucai, et al.
Publicado: (2024)
Stable and Privacy-Preserving Synthetic Educational Data with Empirical Marginals: A Copula-Based Approach
por: Ramos, Gabriel Diaz, et al.
Publicado: (2026)
por: Ramos, Gabriel Diaz, et al.
Publicado: (2026)
Identifying Privacy Personas
por: Hrynenko, Olena, et al.
Publicado: (2024)
por: Hrynenko, Olena, et al.
Publicado: (2024)
Synthetic Data in AI: Challenges, Applications, and Ethical Implications
por: Hao, Shuang, et al.
Publicado: (2024)
por: Hao, Shuang, et al.
Publicado: (2024)
Improving Equity in Health Modeling with GPT4-Turbo Generated Synthetic Data: A Comparative Study
por: Smolyak, Daniel, et al.
Publicado: (2024)
por: Smolyak, Daniel, et al.
Publicado: (2024)
Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes
por: Duong, Manh Khoi, et al.
Publicado: (2024)
por: Duong, Manh Khoi, et al.
Publicado: (2024)
Explainable AI for Predicting and Understanding Mathematics Achievement: A Cross-National Analysis of PISA 2018
por: Liu, Liu, et al.
Publicado: (2025)
por: Liu, Liu, et al.
Publicado: (2025)
Measuring Privacy Risks and Tradeoffs in Financial Synthetic Data Generation
por: Zuo, Michael, et al.
Publicado: (2026)
por: Zuo, Michael, et al.
Publicado: (2026)
Quantitative Auditing of AI Fairness with Differentially Private Synthetic Data
por: Yuan, Chih-Cheng Rex, et al.
Publicado: (2025)
por: Yuan, Chih-Cheng Rex, et al.
Publicado: (2025)
DualAlign: Generating Clinically Grounded Synthetic Data
por: Li, Rumeng, et al.
Publicado: (2025)
por: Li, Rumeng, et al.
Publicado: (2025)
Expected Harm: Rethinking Safety Evaluation of (Mis)Aligned LLMs
por: Chen, Yen-Shan, et al.
Publicado: (2026)
por: Chen, Yen-Shan, et al.
Publicado: (2026)
Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility
por: Wolfrath, Nathan, et al.
Publicado: (2024)
por: Wolfrath, Nathan, et al.
Publicado: (2024)
"They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensing
por: Duan, Moming, et al.
Publicado: (2024)
por: Duan, Moming, et al.
Publicado: (2024)
Evaluating LLMs' Assessment of Mixed-Context Hallucination Through the Lens of Summarization
por: Qi, Siya, et al.
Publicado: (2025)
por: Qi, Siya, et al.
Publicado: (2025)
Alignment as Institutional Design: From Behavioral Correction to Transaction Structure in Intelligent Systems
por: Chai, Rui
Publicado: (2026)
por: Chai, Rui
Publicado: (2026)
Ejemplares similares
-
Are Sparse Autoencoders Useful for Java Function Bug Detection?
por: Melo, Rui, et al.
Publicado: (2025) -
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
por: Zhou, Yan, et al.
Publicado: (2025) -
Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data
por: Hyrup, Tobias, et al.
Publicado: (2023) -
Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
por: Nafis, Nazia, et al.
Publicado: (2025) -
Achievable Fairness on Your Data With Utility Guarantees
por: Taufiq, Muhammad Faaiz, et al.
Publicado: (2024)