Imagen de Portada

Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Amin, Mostafa M., Schuller, Björn W.
Formato:	Preprint
Publicado:	2022
Materias:	Machine Learning Computers and Society
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Identities are not Interchangeable: The Problem of Overgeneralization in Fair Machine Learning
por: Wang, Angelina
Publicado: (2025)

What is Fair? Defining Fairness in Machine Learning for Health
por: Gao, Jianhui, et al.
Publicado: (2024)

Procedural Fairness and Its Relationship with Distributive Fairness in Machine Learning
por: Wang, Ziming, et al.
Publicado: (2025)

AdapFair: Ensuring Adaptive Fairness for Machine Learning Operations
por: Huang, Yinghui, et al.
Publicado: (2024)

The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning
por: Fawkes, Jake, et al.
Publicado: (2024)

Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning
por: Chen, Hongyao, et al.
Publicado: (2025)

An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems
por: Thu, Huyen Giang Thi, et al.
Publicado: (2024)

Machine Learning Techniques with Fairness for Prediction of Completion of Drug and Alcohol Rehabilitation
por: Roberts-Licklider, Karen, et al.
Publicado: (2024)

Insights From Insurance for Fair Machine Learning
por: Fröhlich, Christian, et al.
Publicado: (2023)

Tuning Derivatives for Causal Fairness in Machine Learning
por: Edström, Filip, et al.
Publicado: (2026)

Adaptive Boosting with Fairness-aware Reweighting Technique for Fair Classification
por: Song, Xiaobin, et al.
Publicado: (2024)

Evaluating Fair Feature Selection in Machine Learning for Healthcare
por: Zawad, Md Rahat Shahriar, et al.
Publicado: (2024)

FairHome: A Fair Housing and Fair Lending Dataset
por: Bagalkotkar, Anusha, et al.
Publicado: (2024)

Fair Machine Learning in Healthcare: A Review
por: Feng, Qizhang, et al.
Publicado: (2022)

Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings
por: Lince, M. Generali, et al.
Publicado: (2026)

Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets
por: Kim, Woojin, et al.
Publicado: (2025)

The Effect of Enforcing Fairness on Reshaping Explanations in Machine Learning Models
por: Anderson, Joshua Wolff, et al.
Publicado: (2025)

Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency
por: Haghighat, Parian, et al.
Publicado: (2024)

Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairness
por: Somerstep, Seamus, et al.
Publicado: (2024)

FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning
por: Roy, Arjun, et al.
Publicado: (2023)

Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study
por: Valentine, Alissa A., et al.
Publicado: (2024)

Fairness in Federated Learning: Fairness for Whom?
por: Taik, Afaf, et al.
Publicado: (2025)

On The Fairness Impacts of Hardware Selection in Machine Learning
por: Nelaturu, Sree Harsha, et al.
Publicado: (2023)

Causal Fair Machine Learning via Rank-Preserving Interventional Distributions
por: Bothmann, Ludwig, et al.
Publicado: (2023)

FairWire: Fair Graph Generation
por: Kose, O. Deniz, et al.
Publicado: (2024)

FairGT: A Fairness-aware Graph Transformer
por: Luo, Renqiang, et al.
Publicado: (2024)

Differentially Private Post-Processing for Fair Regression
por: Xian, Ruicheng, et al.
Publicado: (2024)

Fair and Accurate Regression: Strong Formulations and Algorithms
por: Deza, Anna, et al.
Publicado: (2024)

Impact of Fairness Regulations on Institutions' Policies and Population Qualifications
por: Montaseri, Hamidreza, et al.
Publicado: (2024)

Learning Interpretable Fair Representations
por: Wang, Tianhao, et al.
Publicado: (2024)

Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review
por: Benítez-Peña, Sandra, et al.
Publicado: (2026)

FairPFN: A Tabular Foundation Model for Causal Fairness
por: Robertson, Jake, et al.
Publicado: (2025)

Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain
por: Gajane, Pratik, et al.
Publicado: (2024)

Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing
por: Duan, Yuying, et al.
Publicado: (2024)

FairMT: Fairness for Heterogeneous Multi-Task Learning
por: Hu, Guanyu, et al.
Publicado: (2025)

Fair CCA for Fair Representation Learning: An ADNI Study
por: Hou, Bojian, et al.
Publicado: (2025)

Fairness in Reinforcement Learning with Bisimulation Metrics
por: Rezaei-Shoshtari, Sahand, et al.
Publicado: (2024)

Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems
por: Yan, Jing Nathan, et al.
Publicado: (2025)

Kernelised Normalising Flows
por: English, Eshant, et al.
Publicado: (2023)

FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods
por: Han, Xiaotian, et al.
Publicado: (2023)