Fairness Without Harm: An Influence-Guided Active Sampling Approach

Fairness Without Harm: An Influence-Guided Active Sampling Approach

20 February 2024

Zhaowei Zhu

Papers citing "Fairness Without Harm: An Influence-Guided Active Sampling Approach"

7 / 7 papers shown

Title
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning Paradigm Yichen Xie Han Lu Junchi Yan Xiaokang Yang M. Tomizuka Wei Zhan 31 29 0 25 Mar 2023
Bias Mimicking: A Simple Sampling Approach for Bias Mitigation Maan Qraitem Kate Saenko Bryan A. Plummer 43 33 0 30 Sep 2022
Achieving Fairness at No Utility Cost via Data Reweighing with Influence Peizhao Li Hongfu Liu TDI 25 45 0 01 Feb 2022
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning Zhaowei Zhu Tianyi Luo Yang Liu 148 39 0 12 Oct 2021
The Fairness-Accuracy Pareto Front Susan Wei Marc Niethammer FaML 26 33 0 25 Aug 2020
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 189 730 0 13 Dec 2018
SMOTE: Synthetic Minority Over-sampling Technique Nitesh V. Chawla Kevin W. Bowyer Lawrence Hall W. Kegelmeyer AI4TS 148 25,150 0 09 Jun 2011