On the Fairness of Disentangled Representations

31 May 2019

Francesco Locatello

Stefan Bauer

Olivier Bachem

Papers citing "On the Fairness of Disentangled Representations"

28 / 78 papers shown

Title
Where and What? Examining Interpretable Disentangled Representations Xinqi Zhu Chang Xu Dacheng Tao FAtt DRL 58 38 0 07 Apr 2021
AI Fairness via Domain Adaptation Neil J. Joshi Philippe Burlina 29 15 0 15 Mar 2021
Fairness in TabNet Model by Disentangled Representation for the Prediction of Hospital No-Show Sabri Boughorbel Fethi Jarray A. Kadri OOD 30 6 0 06 Mar 2021
Towards Building A Group-based Unsupervised Representation Disentanglement Framework Tao Yang Xuanchi Ren Yuwang Wang W. Zeng Nanning Zheng CoGe DRL 24 27 0 20 Feb 2021
Measuring Disentanglement: A Review of Metrics M. Carbonneau Julian Zaïdi Jonathan Boilard G. Gagnon CoGe DRL 33 81 0 16 Dec 2020
Fairness in Biometrics: a figure of merit to assess biometric verification systems Tiago de Freitas Pereira S´ebastien Marcel 24 62 0 04 Nov 2020
On the Transfer of Disentangled Representations in Realistic Settings Andrea Dittadi Frederik Trauble Francesco Locatello M. Wuthrich Vaibhav Agrawal Ole Winther Stefan Bauer Bernhard Schölkopf OOD 35 80 0 27 Oct 2020
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD 14 66 0 27 Oct 2020
Robust Disentanglement of a Few Factors at a Time Benjamin Estermann Markus Marks M. Yanik CoGe OOD DRL 18 3 0 26 Oct 2020
Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information Enyan Dai Suhang Wang FaML 16 241 0 03 Sep 2020
Privacy-preserving Voice Analysis via Disentangled Representations Ranya Aloufi Hamed Haddadi David E. Boyle DRL 26 58 0 29 Jul 2020
A Commentary on the Unsupervised Learning of Disentangled Representations Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD DRL 29 20 0 28 Jul 2020
Learning Disentangled Representations with Latent Variation Predictability Xinqi Zhu Chang Xu Dacheng Tao CoGe DRL 22 26 0 25 Jul 2020
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding David A. Klindt Lukas Schott Yash Sharma Ivan Ustyuzhaninov Wieland Brendel Matthias Bethge Dylan M. Paiton CML 48 132 0 21 Jul 2020
Investigating Bias and Fairness in Facial Expression Recognition Tian Xu J. White Sinan Kalkan Hatice Gunes CVBM 29 158 0 20 Jul 2020
On Disentangled Representations Learned From Correlated Data Frederik Trauble Elliot Creager Niki Kilbertus Francesco Locatello Andrea Dittadi Anirudh Goyal Bernhard Schölkopf Stefan Bauer OOD CML 29 115 0 14 Jun 2020
Fairness in Forecasting and Learning Linear Dynamical Systems Quan-Gen Zhou Jakub Mareˇcek Robert Shorten AI4TS 32 7 0 12 Jun 2020
Fairness by Learning Orthogonal Disentangled Representations Mhd Hasan Sarhan Nassir Navab Abouzar Eslami Shadi Albarqouni FaML OOD CML 19 96 0 12 Mar 2020
NestedVAE: Isolating Common Factors via Weak Supervision M. Vowels Necati Cihan Camgöz Richard Bowden CML DRL 26 21 0 26 Feb 2020
CheXclusion: Fairness gaps in deep chest X-ray classifiers Laleh Seyyed-Kalantari Guanxiong Liu Matthew B. A. McDermott Irene Y. Chen Marzyeh Ghassemi OOD 37 285 0 14 Feb 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 184 314 0 07 Feb 2020
On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset Muhammad Waleed Gondal Manuel Wüthrich Ðorðe Miladinovic Francesco Locatello M. Breidt V. Volchkov J. Akpo Olivier Bachem Bernhard Schölkopf Stefan Bauer OOD DRL 33 134 0 07 Jun 2019
Unsupervised Model Selection for Variational Disentangled Representation Learning Sunny Duan Loic Matthey Andre Saraiva Nicholas Watters Christopher P. Burgess Alexander Lerchner I. Higgins OOD DRL 16 78 0 29 May 2019
Disentangling Factors of Variation Using Few Labels Francesco Locatello Michael Tschannen Stefan Bauer Gunnar Rätsch Bernhard Schölkopf Olivier Bachem DRL CML CoGe 34 123 0 03 May 2019
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry X. Xing Ruiqi Gao Tian Han Song-Chun Zhu Ying Nian Wu DRL 24 28 0 16 Jun 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 233 675 0 17 Feb 2018
Building machines that adapt and compute like brains Brenden M. Lake J. Tenenbaum AI4CE FedML NAI AILaw 254 889 0 11 Nov 2017
Learning Representations for Counterfactual Inference Fredrik D. Johansson Uri Shalit David Sontag CML OOD BDL 232 720 0 12 May 2016