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Flexibly Fair Representation Learning by Disentanglement

Flexibly Fair Representation Learning by Disentanglement

6 June 2019
Elliot Creager
David Madras
J. Jacobsen
Marissa A. Weis
Kevin Swersky
T. Pitassi
R. Zemel
    FaMLOOD
ArXiv (abs)PDFHTML

Papers citing "Flexibly Fair Representation Learning by Disentanglement"

29 / 29 papers shown
Title
A Generic Framework for Conformal Fairness
A Generic Framework for Conformal Fairness
Aditya T. Vadlamani
Anutam Srinivasan
Pranav Maneriker
Ali Payani
Srinivasan Parthasarathy
FaMLFedML
415
1
0
22 May 2025
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics
Insung Kong
Kunwoong Kim
Yongdai Kim
FaML
172
1
0
09 May 2025
Unbiased GNN Learning via Fairness-Aware Subgraph Diffusion
Abdullah Alchihabi
Yuhong Guo
DiffM
77
0
0
03 Jan 2025
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
Rethinking Fair Representation Learning for Performance-Sensitive Tasks
Charles Jones
Fabio De Sousa Ribeiro
Mélanie Roschewitz
Daniel Coelho De Castro
Ben Glocker
FaMLOODCML
253
2
0
05 Oct 2024
AI-based association analysis for medical imaging using latent-space geometric confounder correction
AI-based association analysis for medical imaging using latent-space geometric confounder correction
Xianjing Liu
Yue Liu
Meike W. Vernooij
E. Wolvius
Gennady V. Roshchupkin
Esther E. Bron
MedIm
105
0
0
03 Oct 2023
Efficient Fair Principal Component Analysis
Efficient Fair Principal Component Analysis
Mohammad Mahdi Kamani
Farzin Haddadpour
R. Forsati
M. Mahdavi
68
37
0
12 Nov 2019
Compositional Fairness Constraints for Graph Embeddings
Compositional Fairness Constraints for Graph Embeddings
A. Bose
William L. Hamilton
FaML
70
259
0
25 May 2019
Learning Latent Subspaces in Variational Autoencoders
Learning Latent Subspaces in Variational Autoencoders
Jack Klys
Jake C. Snell
R. Zemel
SSLDRL
125
140
0
14 Dec 2018
Learning Controllable Fair Representations
Learning Controllable Fair Representations
Jiaming Song
Pratyusha Kalluri
Aditya Grover
Shengjia Zhao
Stefano Ermon
FaML
59
179
0
11 Dec 2018
Challenging Common Assumptions in the Unsupervised Learning of
  Disentangled Representations
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello
Stefan Bauer
Mario Lucic
Gunnar Rätsch
Sylvain Gelly
Bernhard Schölkopf
Olivier Bachem
OOD
124
1,467
0
29 Nov 2018
Excessive Invariance Causes Adversarial Vulnerability
Excessive Invariance Causes Adversarial Vulnerability
J. Jacobsen
Jens Behrmann
R. Zemel
Matthias Bethge
AAML
68
166
0
01 Nov 2018
An Empirical Study of Rich Subgroup Fairness for Machine Learning
An Empirical Study of Rich Subgroup Fairness for Machine Learning
Michael Kearns
Seth Neel
Aaron Roth
Zhiwei Steven Wu
FaML
103
207
0
24 Aug 2018
Analyzing Inverse Problems with Invertible Neural Networks
Analyzing Inverse Problems with Invertible Neural Networks
Lynton Ardizzone
Jakob Kruse
Sebastian J. Wirkert
D. Rahner
E. Pellegrini
R. Klessen
Lena Maier-Hein
Carsten Rother
Ullrich Kothe
66
493
0
14 Aug 2018
Hierarchical VampPrior Variational Fair Auto-Encoder
Hierarchical VampPrior Variational Fair Auto-Encoder
P. Botros
Jakub M. Tomczak
DRL
48
7
0
26 Jun 2018
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification
Michael P. Kim
Amirata Ghorbani
James Zou
MLAU
246
341
0
31 May 2018
Invariant Representations without Adversarial Training
Invariant Representations without Adversarial Training
Daniel Moyer
Shuyang Gao
Rob Brekelmans
Greg Ver Steeg
Aram Galstyan
OOD
64
210
0
24 May 2018
Understanding disentangling in $β$-VAE
Understanding disentangling in βββ-VAE
Christopher P. Burgess
I. Higgins
Arka Pal
Loic Matthey
Nicholas Watters
Guillaume Desjardins
Alexander Lerchner
CoGeDRL
68
830
0
10 Apr 2018
Structured Disentangled Representations
Structured Disentangled Representations
Babak Esmaeili
Hao Wu
Sarthak Jain
Alican Bozkurt
N. Siddharth
Brooks Paige
Dana H. Brooks
Jennifer Dy
Jan-Willem van de Meent
OODCMLBDLDRL
82
166
0
06 Apr 2018
Learning Adversarially Fair and Transferable Representations
Learning Adversarially Fair and Transferable Representations
David Madras
Elliot Creager
T. Pitassi
R. Zemel
FaML
379
684
0
17 Feb 2018
Disentangling by Factorising
Disentangling by Factorising
Hyunjik Kim
A. Mnih
CoGeOOD
62
1,350
0
16 Feb 2018
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup
  Fairness
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Michael Kearns
Seth Neel
Aaron Roth
Zhiwei Steven Wu
FaML
197
778
0
14 Nov 2017
On Fairness and Calibration
On Fairness and Calibration
Geoff Pleiss
Manish Raghavan
Felix Wu
Jon M. Kleinberg
Kilian Q. Weinberger
FaML
200
879
0
06 Sep 2017
Counterfactual Fairness
Counterfactual Fairness
Matt J. Kusner
Joshua R. Loftus
Chris Russell
Ricardo M. A. Silva
FaML
224
1,580
0
20 Mar 2017
Equality of Opportunity in Supervised Learning
Equality of Opportunity in Supervised Learning
Moritz Hardt
Eric Price
Nathan Srebro
FaML
228
4,312
0
07 Oct 2016
Censoring Representations with an Adversary
Censoring Representations with an Adversary
Harrison Edwards
Amos Storkey
AAMLFaML
66
505
0
18 Nov 2015
The Variational Fair Autoencoder
The Variational Fair Autoencoder
Christos Louizos
Kevin Swersky
Yujia Li
Max Welling
R. Zemel
DRL
223
633
0
03 Nov 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.9K
150,115
0
22 Dec 2014
Semi-Supervised Learning with Deep Generative Models
Semi-Supervised Learning with Deep Generative Models
Diederik P. Kingma
Danilo Jimenez Rezende
S. Mohamed
Max Welling
GANSSLBDL
88
2,742
0
20 Jun 2014
Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
452
16,929
0
20 Dec 2013
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