Cover Image

Targeted Learning for Data Fairness

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Asemota, Alexander, Hooker, Giles
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Longitudinal Counterfactuals: Constraints and Opportunities
by: Asemota, Alexander, et al.
Published: (2024)

Targeted Learning for Variable Importance
by: Wang, Xiaohan, et al.
Published: (2024)

Targeted Maximum Likelihood Estimation for Integral Projection Models in Population Ecology
by: Zhou, Yunzhe, et al.
Published: (2024)

Stabilizing Estimates of Shapley Values with Control Variates
by: Goldwasser, Jeremy, et al.
Published: (2023)

Statistical Significance of Feature Importance Rankings
by: Goldwasser, Jeremy, et al.
Published: (2024)

Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks
by: Goldwasser, Jeremy, et al.
Published: (2025)

Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots
by: Xin, Xi, et al.
Published: (2024)

Estimating Implicit Regularization in Deep Learning
by: Rudoler, Joseph H., et al.
Published: (2026)

Statistical Inference for Gradient Boosting Regression
by: Fang, Haimo, et al.
Published: (2025)

Accelerated Inference for Partially Observed Markov Processes using Automatic Differentiation
by: Tan, Kevin, et al.
Published: (2024)

Statistical Inference for Explainable Boosting Machines
by: Fang, Haimo, et al.
Published: (2026)

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

Fairness of Deep Ensembles: On the interplay between per-group task difficulty and under-representation
by: Claucich, Estanislao, et al.
Published: (2025)

Fairness Testing for Algorithmic Pricing
by: Huang, Fei, et al.
Published: (2026)

LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives
by: Shimabucoro, Luísa, et al.
Published: (2024)

Mix Data or Merge Models? Optimizing for Diverse Multi-Task Learning
by: Aakanksha, et al.
Published: (2024)

Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markers
by: D'souza, Daniel, et al.
Published: (2025)

Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data
by: Lammers, Kathrin, et al.
Published: (2025)

Mind the Graph When Balancing Data for Fairness or Robustness
by: Schrouff, Jessica, et al.
Published: (2024)

Generating Synthetic Fair Syntax-agnostic Data by Learning and Distilling Fair Representation
by: Sikder, Md Fahim, et al.
Published: (2024)

On the Limitations of Compute Thresholds as a Governance Strategy
by: Hooker, Sara
Published: (2024)

Fairness-Aware Multi-Group Target Detection in Online Discussion
by: Gupta, Soumyajit, et al.
Published: (2024)

FairTargetSim: An Interactive Simulator for Understanding and Explaining the Fairness Effects of Target Variable Definition
by: Gala, Dalia, et al.
Published: (2024)

Fairness, Accuracy, and Unreliable Data
by: Stangl, Kevin
Published: (2024)

FairFinGAN: Fairness-aware Synthetic Financial Data Generation
by: Quy, Tai Le, et al.
Published: (2026)

Double Fairness Policy Learning: Integrating Action Fairness and Outcome Fairness in Decision-making
by: Bian, Zeyu, et al.
Published: (2026)

A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data
by: Gupta, Devansh, et al.
Published: (2024)

MCRAGE: Synthetic Healthcare Data for Fairness
by: Behal, Keira, et al.
Published: (2023)

FairGRPO: Fair Reinforcement Learning for Equitable Clinical Reasoning
by: Dai, Shiqi, et al.
Published: (2025)

Hate Speech Detection with Generalizable Target-aware Fairness
by: Chen, Tong, et al.
Published: (2024)

SoftSRV: Learn to Generate Targeted Synthetic Data
by: DeSalvo, Giulia, et al.
Published: (2024)

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

Data Acquisition for Improving Model Fairness using Reinforcement Learning
by: Hasan, Jahid, et al.
Published: (2024)

Fairness Regularization in Federated Learning
by: Kharaghani, Zahra, et al.
Published: (2025)

Procedural Fairness in Machine Learning
by: Wang, Ziming, et al.
Published: (2024)

Adversarial Bias: Data Poisoning Attacks on Fairness
by: Chan, Eunice, et al.
Published: (2025)

FairContrast: Enhancing Fairness through Contrastive learning and Customized Augmenting Methods on Tabular Data
by: Tayebi, Aida, et al.
Published: (2025)

How Proxy Race Distorts Regression-Based Fairness Audits
by: Xin, Xi, et al.
Published: (2026)

FairEnergy: Contribution-Based Fairness meets Energy Efficiency in Federated Learning
by: Marnissi, Ouiame, et al.
Published: (2025)

Fairness May Backfire: When Leveling-Down Occurs in Fair Machine Learning
by: Yang, Yi, et al.
Published: (2026)