Copertina

Learning Interpretable Fair Representations

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Wang, Tianhao, Buçinca, Zana, Ma, Zilin
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Computers and Society
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Learning Fair Representations with Kolmogorov-Arnold Networks
di: Priyadarshini, Amisha, et al.
Pubblicazione: (2025)

Fairness-Aware Graph Representation Learning with Limited Demographic Information
di: Wang, Zichong, et al.
Pubblicazione: (2025)

Rethinking Fair Representation Learning for Performance-Sensitive Tasks
di: Jones, Charles, et al.
Pubblicazione: (2024)

Fair Graph Representation Learning via Sensitive Attribute Disentanglement
di: Zhu, Yuchang, et al.
Pubblicazione: (2024)

Learning Fairer Representations with FairVIC
di: Barker, Charmaine, et al.
Pubblicazione: (2024)

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

Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously
di: Li, Dong, et al.
Pubblicazione: (2024)

Fairness-Aware Interpretable Modeling (FAIM) for Trustworthy Machine Learning in Healthcare
di: Liu, Mingxuan, et al.
Pubblicazione: (2024)

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

Closing the Gap in the Trade-off between Fair Representations and Accuracy
di: Rout, Biswajit, et al.
Pubblicazione: (2024)

Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
di: Qian, Xiaowei, et al.
Pubblicazione: (2024)

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

Causal Manifold Fairness: Enforcing Geometric Invariance in Representation Learning
di: Rathore, Vidhi
Pubblicazione: (2026)

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

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

Fair Recommendations with Limited Sensitive Attributes: A Distributionally Robust Optimization Approach
di: Shi, Tianhao, et al.
Pubblicazione: (2024)

On Demographic Group Fairness Guarantees in Deep Learning
di: Luo, Yan, et al.
Pubblicazione: (2024)

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

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

FairEHR-CLP: Towards Fairness-Aware Clinical Predictions with Contrastive Learning in Multimodal Electronic Health Records
di: Wang, Yuqing, et al.
Pubblicazione: (2024)

Distribution-Free Fair Federated Learning with Small Samples
di: Yin, Qichuan, et al.
Pubblicazione: (2024)

Fair Clustering with Minimum Representation Constraints
di: Lawless, Connor, et al.
Pubblicazione: (2024)

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

Fair Representation in Parliamentary Summaries: Measuring and Mitigating Inclusion Bias
di: Cunningham, Eoghan, et al.
Pubblicazione: (2025)

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

CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs
di: Arun, Arvindh, et al.
Pubblicazione: (2023)

Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems
di: Amin, Mostafa M., et al.
Pubblicazione: (2022)

BoostFGL: Boosting Fairness in Federated Graph Learning
di: Chen, Zekai, et al.
Pubblicazione: (2026)

Balancing the Scales: Reinforcement Learning for Fair Classification
di: Eshuijs, Leon, et al.
Pubblicazione: (2024)

Consistent End-to-End Estimation for Counterfactual Fairness
di: Ma, Yuchen, et al.
Pubblicazione: (2023)

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

Semi-Variance Reduction for Fair Federated Learning
di: Malekmohammadi, Saber, et al.
Pubblicazione: (2024)

Fairness in Federated Learning: Trends, Challenges, and Opportunities
di: Mukhtiar, Noorain, et al.
Pubblicazione: (2025)

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

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

Interpreting Latent Student Knowledge Representations in Programming Assignments
di: Fernandez, Nigel, et al.
Pubblicazione: (2024)

FairWire: Fair Graph Generation
di: Kose, O. Deniz, et al.
Pubblicazione: (2024)

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

Referee-Meta-Learning for Fast Adaptation of Locational Fairness
di: Chen, Weiye, et al.
Pubblicazione: (2024)