Can Less be More? When Increasing-to-Balancing Label Noise Rates
Considered Beneficial

Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial

13 July 2021

Yang Liu

Papers citing "Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial"

6 / 6 papers shown

Title
Understanding Instance-Level Impact of Fairness Constraints Jialu Wang Xinze Wang Yang Liu TDI FaML 28 33 0 30 Jun 2022
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features Zhaowei Zhu Jialu Wang Yang Liu NoLa 38 37 0 02 Feb 2022
Detecting Corrupted Labels Without Training a Model to Predict Zhaowei Zhu Zihao Dong Yang Liu NoLa 149 62 0 12 Oct 2021
To Smooth or Not? When Label Smoothing Meets Noisy Labels Jiaheng Wei Hangyu Liu Tongliang Liu Gang Niu Masashi Sugiyama Yang Liu NoLa 32 69 0 08 Jun 2021
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information Pranjal Awasthi Alex Beutel Matthaeus Kleindessner Jamie Morgenstern Xuezhi Wang FaML 54 55 0 16 Feb 2021
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,092 0 24 Oct 2016