Cover Image

Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kong, Insung, Kim, Kunwoong, Kim, Yongdai
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

ReLU integral probability metric and its applications
by: Park, Yuha, et al.
Published: (2025)

Fairness Through Matching
by: Kim, Kunwoong, et al.
Published: (2025)

Fair Bayesian Model-Based Clustering
by: Lee, Jihu, et al.
Published: (2025)

Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights
by: Kong, Insung, et al.
Published: (2024)

Fair Model-based Clustering
by: Park, Jinwon, et al.
Published: (2026)

Fair Clustering via Alignment
by: Kim, Kunwoong, et al.
Published: (2025)

Doubly-Regressing Approach for Subgroup Fairness
by: Kim, Kunwoong, et al.
Published: (2025)

Bayesian Additive Regression Trees for functional ANOVA model
by: Park, Seokhun, et al.
Published: (2025)

SLIDE: a surrogate fairness constraint to ensure fairness consistency
by: Kim, Kunwoong, et al.
Published: (2022)

A Composite Activation Function for Learning Stable Binary Representations
by: Park, Seokhun, et al.
Published: (2026)

ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models
by: Kim, Dongha, et al.
Published: (2023)

Bayesian Neural Networks for Functional ANOVA model
by: Park, Seokhun, et al.
Published: (2025)

Tensor Product Neural Networks for Functional ANOVA Model
by: Park, Seokhun, et al.
Published: (2025)

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

Integral Imprecise Probability Metrics
by: Chau, Siu Lun, et al.
Published: (2025)

TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification
by: Yang, Dongyoon, et al.
Published: (2025)

Generative Modeling of Class Probability for Multi-Modal Representation Learning
by: Shin, Jungkyoo, et al.
Published: (2025)

Knowledge Distillation of Uncertainty using Deep Latent Factor Model
by: Park, Sehyun, et al.
Published: (2025)

Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes
by: Sohn, Jinwon, et al.
Published: (2023)

Fairness without Sensitive Attributes via Knowledge Sharing
by: Ni, Hongliang, et al.
Published: (2024)

On the Universal Representation Property of Spiking Neural Networks
by: Hundrieser, Shayan, et al.
Published: (2025)

META-ANOVA: Screening interactions for interpretable machine learning
by: Choi, Yongchan, et al.
Published: (2024)

Rethinking Evaluation Metric for Probability Estimation Models Using Esports Data
by: Choi, Euihyeon, et al.
Published: (2023)

One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes
by: Zhu, Yuchang, et al.
Published: (2024)

A Sequentially Fair Mechanism for Multiple Sensitive Attributes
by: Hu, François, et al.
Published: (2023)

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

Dynamic Time-aware Continual User Representation Learning
by: Choi, Seungyoon, et al.
Published: (2025)

Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective
by: Kim, Joonkyu, et al.
Published: (2025)

Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test
by: Paik, Seunghoon, et al.
Published: (2023)

Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models
by: Yang, Yuning, et al.
Published: (2025)

Learning Fair Models without Sensitive Attributes: A Generative Approach
by: Zhu, Huaisheng, et al.
Published: (2022)

Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport
by: Hundrieser, Shayan, et al.
Published: (2026)

Can Machines Learn the True Probabilities?
by: Kim, Jinsook
Published: (2024)

Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning
by: Li, Yanli, et al.
Published: (2024)

Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
by: Veldanda, Akshaj Kumar, et al.
Published: (2023)

Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks
by: Shi, Enze, et al.
Published: (2025)

Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes
by: Wang, Xuemin, et al.
Published: (2024)

Scalable Deep Metric Learning on Attributed Graphs
by: Li, Xiang, et al.
Published: (2024)

Fair Class-Incremental Learning using Sample Weighting
by: Park, Jaeyoung, et al.
Published: (2024)

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