v1v2v3 (latest)

Censoring Representations with an Adversary

18 November 2015

Amos Storkey

Papers citing "Censoring Representations with an Adversary"

50 / 205 papers shown

Title
Compositional Fairness Constraints for Graph Embeddings A. Bose William L. Hamilton FaML 110 259 0 25 May 2019
Learning Fair Representations via an Adversarial Framework Rui Feng Yang Yang Yuehan Lyu Chenhao Tan Yizhou Sun Chunping Wang FaML 80 56 0 30 Apr 2019
Mitigating Information Leakage in Image Representations: A Maximum Entropy Approach P. Roy Vishnu Boddeti 60 101 0 11 Apr 2019
Revealing Scenes by Inverting Structure from Motion Reconstructions Francesco Pittaluga S. Koppal S. B. Kang Sudipta N. Sinha 3DPC 72 127 0 05 Apr 2019
Adversarial Deep Learning in EEG Biometrics Ozan Özdenizci Ye Wang T. Koike-Akino Deniz Erdogmus 61 77 0 27 Mar 2019
Fairness in Recommendation Ranking through Pairwise Comparisons Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Li Wei ... Lukasz Heldt Zhe Zhao Lichan Hong Ed H. Chi Cristos Goodrow FaML 116 381 0 02 Mar 2019
Adversarial Training for Satire Detection: Controlling for Confounding Variables R. McHardy Heike Adel Roman Klinger 83 32 0 28 Feb 2019
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods Maher Nouiehed Maziar Sanjabi Tianjian Huang Jason D. Lee Meisam Razaviyayn 104 344 0 21 Feb 2019
Fairness in representation: quantifying stereotyping as a representational harm Mohsen Abbasi Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian 53 51 0 28 Jan 2019
Identifying and Correcting Label Bias in Machine Learning Heinrich Jiang Ofir Nachum FaML 104 284 0 15 Jan 2019
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Allison Woodruff Christine Luu Pierre Kreitmann Jonathan Bischof Ed H. Chi FaML 108 153 0 14 Jan 2019
Contamination Attacks and Mitigation in Multi-Party Machine Learning Jamie Hayes O. Ohrimenko AAML FedML 114 75 0 08 Jan 2019
Application-driven Privacy-preserving Data Publishing with Correlated Attributes A. Rezaei Chaowei Xiao Jie Gao Yue Liu Sirajum Munir 45 14 0 26 Dec 2018
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders Ozan Özdenizci Ye Wang T. Koike-Akino Deniz Erdogmus OOD DRL 51 59 0 17 Dec 2018
Learning Latent Subspaces in Variational Autoencoders Jack Klys Jake C. Snell R. Zemel SSL DRL 138 141 0 14 Dec 2018
Learning Controllable Fair Representations Jiaming Song Pratyusha Kalluri Aditya Grover Shengjia Zhao Stefano Ermon FaML 87 180 0 11 Dec 2018
State of the Art in Fair ML: From Moral Philosophy and Legislation to Fair Classifiers Elias Baumann J. L. Rumberger FaML 38 4 0 20 Nov 2018
Mobile Sensor Data Anonymization Mohammad Malekzadeh R. Clegg Andrea Cavallaro Hamed Haddadi 190 212 0 26 Oct 2018
The Frontiers of Fairness in Machine Learning Alexandra Chouldechova Aaron Roth FaML 205 416 0 20 Oct 2018
Discovering Fair Representations in the Data Domain Novi Quadrianto V. Sharmanska Oliver Thomas 71 3 0 15 Oct 2018
Adversarial Recommendation: Attack of the Learned Fake Users Konstantina Christakopoulou A. Banerjee AAML 47 12 0 21 Sep 2018
Creating Fair Models of Atherosclerotic Cardiovascular Disease Risk Stephen Pfohl Ben J. Marafino Adrien Coulet F. Rodriguez L. Palaniappan N. Shah 59 68 0 12 Sep 2018
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts Samuel Carton Qiaozhu Mei Paul Resnick FAtt AAML 124 34 0 01 Sep 2018
Adversarial Removal of Demographic Attributes from Text Data Yanai Elazar Yoav Goldberg FaML 115 309 0 20 Aug 2018
Generative Adversarial Privacy Chong Huang Peter Kairouz Xiao Chen Lalitha Sankar Ram Rajagopal PICV 101 42 0 13 Jul 2018
Gradient Reversal Against Discrimination Edward Raff Jared Sylvester 68 38 0 01 Jul 2018
What About Applied Fairness? Jared Sylvester Edward Raff FaML 139 10 0 13 Jun 2018
Causal Interventions for Fairness Matt J. Kusner Chris Russell Joshua R. Loftus Ricardo M. A. Silva FaML 133 14 0 06 Jun 2018
iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making Preethi Lahoti Krishna P. Gummadi Gerhard Weikum FaML 95 171 0 04 Jun 2018
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning Jihun Hamm Yung-Kyun Noh 79 9 0 29 May 2018
FairGAN: Fairness-aware Generative Adversarial Networks Depeng Xu Shuhan Yuan Lu Zhang Xintao Wu GAN 134 315 0 28 May 2018
Fairness GAN P. Sattigeri Samuel C. Hoffman Vijil Chenthamarakshan Kush R. Varshney 131 93 0 24 May 2018
Causal Reasoning for Algorithmic Fairness Joshua R. Loftus Chris Russell Matt J. Kusner Ricardo M. A. Silva FaML CML 86 128 0 15 May 2018
Exploiting Unintended Feature Leakage in Collaborative Learning Luca Melis Congzheng Song Emiliano De Cristofaro Vitaly Shmatikov FedML 167 1,488 0 10 May 2018
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders A. Jha Saket Anand Maneesh Kumar Singh V. Veeravasarapu CoGe DRL 66 128 0 27 Apr 2018
Path-Specific Counterfactual Fairness Silvia Chiappa Thomas P. S. Gillam CML FaML 95 341 0 22 Feb 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 388 685 0 17 Feb 2018
Fair Forests: Regularized Tree Induction to Minimize Model Bias Edward Raff Jared Sylvester S. Mills FaML 68 71 0 21 Dec 2017
Privacy-Preserving Adversarial Networks Ardhendu Shekhar Tripathy Ye Wang Prakash Ishwar PICV 95 84 0 19 Dec 2017
Context-Aware Generative Adversarial Privacy Chong Huang Peter Kairouz Xiao Chen Lalitha Sankar Ram Rajagopal 107 159 0 26 Oct 2017
Provably Fair Representations D. McNamara Cheng Soon Ong Robert C. Williamson FaML 68 55 0 12 Oct 2017
On Fairness and Calibration Geoff Pleiss Manish Raghavan Felix Wu Jon M. Kleinberg Kilian Q. Weinberger FaML 212 882 0 06 Sep 2017
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses Ryan J. Lowe Michael Noseworthy Iulian Serban Nicolas Angelard-Gontier Yoshua Bengio Joelle Pineau 57 372 0 23 Aug 2017
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations Alex Beutel Jilin Chen Zhe Zhao Ed H. Chi FaML 121 442 0 01 Jul 2017
Avoiding Discrimination through Causal Reasoning Niki Kilbertus Mateo Rojas-Carulla Giambattista Parascandolo Moritz Hardt Dominik Janzing Bernhard Schölkopf FaML CML 119 584 0 08 Jun 2017
Fader Networks: Manipulating Images by Sliding Attributes Guillaume Lample Neil Zeghidour Nicolas Usunier Antoine Bordes Ludovic Denoyer MarcÁurelio Ranzato DRL GAN 119 546 0 01 Jun 2017
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations Diane Bouchacourt Ryota Tomioka Sebastian Nowozin BDL OOD DRL 80 314 0 24 May 2017
Stabilizing Adversarial Nets With Prediction Methods A. Yadav Sohil Shah Zheng Xu David Jacobs Tom Goldstein ODL 102 89 0 20 May 2017
NIPS 2016 Tutorial: Generative Adversarial Networks Ian Goodfellow GAN 185 1,727 0 31 Dec 2016
Towards the Science of Security and Privacy in Machine Learning Nicolas Papernot Patrick McDaniel Arunesh Sinha Michael P. Wellman AAML 99 474 0 11 Nov 2016