Kernel Dependence Regularizers and Gaussian Processes with Applications
to Algorithmic Fairness

Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness

11 November 2019

Adrián Pérez-Suay

Gustau Camps-Valls

Dino Sejdinovic

Papers citing "Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness"

9 / 9 papers shown

Title
Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing Yezi Liu Prathyush Poduval Wenjun Huang Yang Ni Hanning Chen Mohsen Imani MU 45 0 0 14 May 2025
A statistical approach to detect sensitive features in a group fairness setting G. D. Pelegrina Miguel Couceiro L. Duarte 19 3 0 11 May 2023
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge S. Bouabid Jake Fawkes Dino Sejdinovic CML 49 0 0 26 Jan 2023
Non-Gaussian Gaussian Processes for Few-Shot Regression Marcin Sendera Jacek Tabor A. Nowak Andrzej Bedychaj Massimiliano Patacchiola Tomasz Trzciñski Przemysław Spurek Maciej Ziȩba 23 19 0 26 Oct 2021
RKHS-SHAP: Shapley Values for Kernel Methods Siu Lun Chau Robert Hu Javier I. González Dino Sejdinovic FAtt 26 16 0 18 Oct 2021
Review of Mathematical frameworks for Fairness in Machine Learning E. del Barrio Paula Gordaliza Jean-Michel Loubes FaML FedML 15 39 0 26 May 2020
Spectral Ranking with Covariates Siu Lun Chau Mihai Cucuringu Dino Sejdinovic 16 9 0 08 May 2020
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 236 676 0 17 Feb 2018
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,092 0 24 Oct 2016