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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
Jialu Wang
    NoLa
ArXivPDFHTML

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

4 / 4 papers shown
Title
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with
  Lower-Quality Features
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
Zhaowei Zhu
Jialu Wang
Yang Liu
NoLa
19
37
0
02 Feb 2022
To Smooth or Not? When Label Smoothing Meets Noisy Labels
To Smooth or Not? When Label Smoothing Meets Noisy Labels
Jiaheng Wei
Hangyu Liu
Tongliang Liu
Gang Niu
Masashi Sugiyama
Yang Liu
NoLa
20
69
0
08 Jun 2021
Evaluating Fairness of Machine Learning Models Under Uncertain and
  Incomplete Information
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
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
185
2,082
0
24 Oct 2016
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