:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Hassanpour, Ahmad, Zarei, Amir, Mallat, Khawla, de Oliveira, Anderson Santana, Yang, Bian
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	https://arxiv.org/abs/2412.11951
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

An Empirical Analysis of Fairness Notions under Differential Privacy
por: de Oliveira, Anderson Santana, et al.
Publicado: (2023)

PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
por: Corbucci, Luca, et al.
Publicado: (2024)

Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare
por: Nagesh, Nitish, et al.
Publicado: (2026)

OPUS-VFL: Incentivizing Optimal Privacy-Utility Tradeoffs in Vertical Federated Learning
por: Madabushi, Sindhuja, et al.
Publicado: (2025)

Transformer-Based Representation Learning for Robust Gene Expression Modeling and Cancer Prognosis
por: Jiang, Shuai, et al.
Publicado: (2025)

FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility
por: Sun, Kangkang, et al.
Publicado: (2025)

FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation
por: Nagesh, Nitish, et al.
Publicado: (2025)

PFGuard: A Generative Framework with Privacy and Fairness Safeguards
por: Kim, Soyeon, et al.
Publicado: (2024)

Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
por: Osuala, Richard, et al.
Publicado: (2024)

FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
por: Luo, Renqiang, et al.
Publicado: (2024)

Beyond Rebalancing: Benchmarking Binary Classifiers Under Class Imbalance Without Rebalancing Techniques
por: Nawaz, Ali, et al.
Publicado: (2025)

Privacy at a Price: Exploring its Dual Impact on AI Fairness
por: Yang, Mengmeng, et al.
Publicado: (2024)

Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning
por: Xie, Sean, et al.
Publicado: (2022)

Federated Learning with Differential Privacy: An Utility-Enhanced Approach
por: Ranaweera, Kanishka, et al.
Publicado: (2025)

FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentation
por: Zhu, Lin, et al.
Publicado: (2025)

On the Fairness of Privacy Protection: Measuring and Mitigating the Disparity of Group Privacy Risks for Differentially Private Machine Learning
por: Yang, Zhi, et al.
Publicado: (2025)

Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
por: Zhang, Xiaojin, et al.
Publicado: (2023)

ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities
por: Hassanpour, Ahmad, et al.
Publicado: (2024)

FairTabGen: High-Fidelity and Fair Synthetic Health Data Generation from Limited Samples
por: Nagesh, Nitish, et al.
Publicado: (2025)

Trusting Fair Data: Leveraging Quality in Fairness-Driven Data Removal Techniques
por: Duong, Manh Khoi, et al.
Publicado: (2024)

Individual Fairness In Strategic Classification
por: Zuo, Zhiqun, et al.
Publicado: (2026)

Cumulative Utility Parity for Fair Federated Learning under Intermittent Client Participation
por: Behfar, Stefan, et al.
Publicado: (2026)

INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy
por: Tian, Xiao, et al.
Publicado: (2026)

Decomposed Trust: Privacy, Adversarial Robustness, Ethics, and Fairness in Low-Rank LLMs
por: Asante, Daniel Agyei, et al.
Publicado: (2025)

A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
por: Gu, Hanlin, et al.
Publicado: (2023)

GFLC: Graph-based Fairness-aware Label Correction for Fair Classification
por: Sulaiman, Modar, et al.
Publicado: (2025)

Multi-Objective Optimization for Privacy-Utility Balance in Differentially Private Federated Learning
por: Ranaweera, Kanishka, et al.
Publicado: (2025)

When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks
por: Tian, Huan, et al.
Publicado: (2023)

Reasoning Distillation and Structural Alignment for Improved Code Generation
por: Jalilifard, Amir, et al.
Publicado: (2025)

IFFair: Influence Function-driven Sample Reweighting for Fair Classification
por: Yang, Jingran, et al.
Publicado: (2025)

E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space
por: Hassanpour, Ahmad, et al.
Publicado: (2024)

Investigating the Interplay of Prioritized Replay and Generalization
por: Panahi, Parham Mohammad, et al.
Publicado: (2024)

A Multivocal Literature Review on Privacy and Fairness in Federated Learning
por: Balbierer, Beatrice, et al.
Publicado: (2024)

FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization
por: Dai, Yucong, et al.
Publicado: (2025)

A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models
por: Yang, Junjie, et al.
Publicado: (2024)

Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
por: Lv, Xinpeng, et al.
Publicado: (2026)

Single Parent Family: A Spectrum of Family Members from a Single Pre-Trained Foundation Model
por: Hajimolahoseini, Habib, et al.
Publicado: (2024)

Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification
por: Chaddad, Ahmad, et al.
Publicado: (2025)

Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives
por: Queiroz, Dilermando, et al.
Publicado: (2025)

FairTTTS: A Tree Test Time Simulation Method for Fairness-Aware Classification
por: Cohen-Inger, Nurit, et al.
Publicado: (2025)