Inherent Tradeoffs in Learning Fair Representations

19 June 2019

Papers citing "Inherent Tradeoffs in Learning Fair Representations"

21 / 21 papers shown

Title
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 53 0 0 28 Feb 2025
Fair Resource Allocation in Weakly Coupled Markov Decision Processes Xiaohui Tu Yossiri Adulyasak Nima Akbarzadeh Erick Delage 29 0 0 14 Nov 2024
Rethinking Fair Representation Learning for Performance-Sensitive Tasks Charles Jones Fabio De Sousa Ribeiro Mélanie Roschewitz Daniel Coelho De Castro Ben Glocker FaML OOD CML 57 1 0 05 Oct 2024
Is On-Device AI Broken and Exploitable? Assessing the Trust and Ethics in Small Language Models Kalyan Nakka Jimmy Dani Nitesh Saxena 37 1 0 08 Jun 2024
Learning to Generate Equitable Text in Dialogue from Biased Training Data Anthony Sicilia Malihe Alikhani 30 15 0 10 Jul 2023
Fairness Improves Learning from Noisily Labeled Long-Tailed Data Jiaheng Wei Zhaowei Zhu Gang Niu Tongliang Liu Sijia Liu Masashi Sugiyama Yang Liu 19 6 0 22 Mar 2023
Fairness-aware Regression Robust to Adversarial Attacks Yulu Jin Lifeng Lai FaML OOD 8 4 0 04 Nov 2022
To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier Camille Olivia Little Michael Weylandt Genevera I. Allen 14 13 0 31 May 2022
Survey on Fair Reinforcement Learning: Theory and Practice Pratik Gajane A. Saxena M. Tavakol George Fletcher Mykola Pechenizkiy FaML OffRL 15 13 0 20 May 2022
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey Ferdinando Fioretto Cuong Tran Pascal Van Hentenryck Keyu Zhu FaML 11 59 0 16 Feb 2022
Group-Aware Threshold Adaptation for Fair Classification T. Jang P. Shi Xiaoqian Wang FaML 71 35 0 08 Nov 2021
Fair Representation: Guaranteeing Approximate Multiple Group Fairness for Unknown Tasks Xudong Shen Yongkang Wong Mohan S. Kankanhalli FaML 11 20 0 01 Sep 2021
Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data Paul Pu Liang Terrance Liu Anna Cai Michal Muszynski Ryo Ishii Nicholas B. Allen Randy P. Auerbach David Brent Ruslan Salakhutdinov Louis-Philippe Morency 30 16 0 24 Jun 2021
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning Yuyan Wang Xuezhi Wang Alex Beutel Flavien Prost Jilin Chen Ed H. Chi FaML 11 46 0 04 Jun 2021
Representative & Fair Synthetic Data P. Tiwald Alexandra Ebert Daniel Soukup 17 12 0 07 Apr 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 225 485 0 31 Dec 2020
On Learning Language-Invariant Representations for Universal Machine Translation Hao Zhao Junjie Hu Andrej Risteski 20 8 0 11 Aug 2020
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 208 663 0 17 Feb 2018
A statistical framework for fair predictive algorithms K. Lum J. Johndrow FaML 165 103 0 25 Oct 2016
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,079 0 24 Oct 2016
Domain Adaptation: Learning Bounds and Algorithms Yishay Mansour M. Mohri Afshin Rostamizadeh 179 786 0 19 Feb 2009