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Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task
  Learning

Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning

4 June 2021
Yuyan Wang
Xuezhi Wang
Alex Beutel
Flavien Prost
Jilin Chen
Ed H. Chi
    FaML
ArXivPDFHTML

Papers citing "Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning"

6 / 6 papers shown
Title
Justified Evidence Collection for Argument-based AI Fairness Assurance
Justified Evidence Collection for Argument-based AI Fairness Assurance
Alpay Sabuncuoglu
Christopher Burr
Carsten Maple
29
0
0
12 May 2025
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
Ruta Binkyte
Ivaxi Sheth
Zhijing Jin
Mohammad Havaei
Bernhard Schölkopf
Mario Fritz
128
0
0
28 Feb 2025
Fairness in Multi-Task Learning via Wasserstein Barycenters
Fairness in Multi-Task Learning via Wasserstein Barycenters
Franccois Hu
Philipp Ratz
Arthur Charpentier
35
10
0
16 Jun 2023
On Learning Fairness and Accuracy on Multiple Subgroups
On Learning Fairness and Accuracy on Multiple Subgroups
Changjian Shui
Gezheng Xu
Qi Chen
Jiaqi Li
Charles X. Ling
Tal Arbel
Boyu Wang
Christian Gagné
39
37
0
19 Oct 2022
Survey on Fair Reinforcement Learning: Theory and Practice
Survey on Fair Reinforcement Learning: Theory and Practice
Pratik Gajane
A. Saxena
M. Tavakol
George Fletcher
Mykola Pechenizkiy
FaML
OffRL
35
13
0
20 May 2022
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
233
673
0
17 Feb 2018
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