Flexibly Fair Representation Learning by Disentanglement

6 June 2019

Papers citing "Flexibly Fair Representation Learning by Disentanglement"

36 / 86 papers shown

Title
Learning Disentangled Representations in the Imaging Domain Xiao Liu Pedro Sanchez Spyridon Thermos Alison Q. OÑeil Sotirios A. Tsaftaris OOD DRL 32 71 0 26 Aug 2021
Impossibility results for fair representations Tosca Lechner Shai Ben-David Sushant Agarwal Nivasini Ananthakrishnan FaML 24 14 0 07 Jul 2021
Projection-wise Disentangling for Fair and Interpretable Representation Learning: Application to 3D Facial Shape Analysis Xianjing Liu Bo Li Esther E. Bron W. Niessen E. Wolvius Gennady Roshchupkin CVBM 32 9 0 25 Jun 2021
Fair Normalizing Flows Mislav Balunović Anian Ruoss Martin Vechev AAML 24 36 0 10 Jun 2021
Fair Representations by Compression Xavier Gitiaux Huzefa Rangwala FaML 30 14 0 28 May 2021
Fair Feature Distillation for Visual Recognition S. Jung Donggyu Lee Taeeon Park Taesup Moon 27 75 0 27 May 2021
Discover the Unknown Biased Attribute of an Image Classifier Zhiheng Li Chenliang Xu 40 50 0 29 Apr 2021
Where and What? Examining Interpretable Disentangled Representations Xinqi Zhu Chang Xu Dacheng Tao FAtt DRL 61 38 0 07 Apr 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
Improving the Fairness of Deep Generative Models without Retraining Shuhan Tan Yujun Shen Bolei Zhou 183 59 0 09 Dec 2020
Minimax Pareto Fairness: A Multi Objective Perspective Natalia Martínez Martín Bertrán Guillermo Sapiro FaML 25 190 0 03 Nov 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 31 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 32 20 0 28 Jul 2020
Data-efficient visuomotor policy training using reinforcement learning and generative models Ali Ghadirzadeh Petra Poklukar Ville Kyrki Danica Kragic Mårten Björkman OffRL 46 9 0 26 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
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
A Variational Approach to Privacy and Fairness Borja Rodríguez Gálvez Ragnar Thobaben Mikael Skoglund FaML DRL 21 25 0 11 Jun 2020
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges D. Charte F. Charte M. J. D. Jesus Francisco Herrera SSL OOD 27 51 0 21 May 2020
Risk of Training Diagnostic Algorithms on Data with Demographic Bias Samaneh Abbasi-Sureshjani Ralf Raumanns B. Michels Gerard Schouten Veronika Cheplygina FaML 38 35 0 20 May 2020
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space Yingyi Ma Vignesh Ganapathiraman Yaoliang Yu Xinhua Zhang 21 1 0 25 Apr 2020
Guided Variational Autoencoder for Disentanglement Learning Zheng Ding Yifan Xu Weijian Xu Gaurav Parmar Yang Yang Max Welling Zhuowen Tu DRL CoGe 34 106 0 02 Apr 2020
Fairness by Learning Orthogonal Disentangled Representations Mhd Hasan Sarhan Nassir Navab Abouzar Eslami Shadi Albarqouni FaML OOD CML 27 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
Learning Certified Individually Fair Representations Anian Ruoss Mislav Balunović Marc Fischer Martin Vechev FaML 15 92 0 24 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
Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation Zeyu Wang Klint Qinami Yannis Karakozis Kyle Genova P. Nair Kenji Hata Olga Russakovsky 38 357 0 26 Nov 2019
Efficient Fair Principal Component Analysis Mohammad Mahdi Kamani Farzin Haddadpour R. Forsati M. Mahdavi 13 36 0 12 Nov 2019
Auditing and Achieving Intersectional Fairness in Classification Problems Giulio Morina V. Oliinyk J. Waton Ines Marusic K. Georgatzis FaML 19 39 0 04 Nov 2019
Conditional Learning of Fair Representations Han Zhao Amanda Coston T. Adel Geoffrey J. Gordon FaML 35 106 0 16 Oct 2019
Learning De-biased Representations with Biased Representations Hyojin Bahng Sanghyuk Chun Sangdoo Yun Jaegul Choo Seong Joon Oh OOD 312 276 0 07 Oct 2019
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 355 4,237 0 23 Aug 2019
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
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 236 676 0 17 Feb 2018