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Unbiased Risk Estimators Can Mislead: A Case Study of Learning with
  Complementary Labels
v1v2v3 (latest)

Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels

5 July 2020
Yu-Ting Chou
Gang Niu
Hsuan-Tien Lin
Masashi Sugiyama
ArXiv (abs)PDFHTML

Papers citing "Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels"

25 / 25 papers shown
Title
Learning with Multiple Complementary Labels
Learning with Multiple Complementary Labels
Lei Feng
Takuo Kaneko
Bo Han
Gang Niu
Bo An
Masashi Sugiyama
75
98
0
30 Dec 2019
Mitigating Overfitting in Supervised Classification from Two Unlabeled
  Datasets: A Consistent Risk Correction Approach
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
Nan Lu
Tianyi Zhang
Gang Niu
Masashi Sugiyama
54
55
0
20 Oct 2019
NLNL: Negative Learning for Noisy Labels
NLNL: Negative Learning for Noisy Labels
Youngdong Kim
Junho Yim
Juseung Yun
Junmo Kim
NoLa
52
277
0
19 Aug 2019
Are Anchor Points Really Indispensable in Label-Noise Learning?
Are Anchor Points Really Indispensable in Label-Noise Learning?
Xiaobo Xia
Tongliang Liu
N. Wang
Bo Han
Chen Gong
Gang Niu
Masashi Sugiyama
NoLa
73
381
0
01 Jun 2019
Generative-Discriminative Complementary Learning
Generative-Discriminative Complementary Learning
Yanwu Xu
Biwei Huang
Junxiang Chen
Tongliang Liu
Kun Zhang
Kayhan Batmanghelich
GAN
40
38
0
02 Apr 2019
Uniform convergence may be unable to explain generalization in deep
  learning
Uniform convergence may be unable to explain generalization in deep learning
Vaishnavh Nagarajan
J. Zico Kolter
MoMeAI4CE
81
315
0
13 Feb 2019
Online Multiclass Classification Based on Prediction Margin for Partial
  Feedback
Online Multiclass Classification Based on Prediction Margin for Partial Feedback
Takuo Kaneko
Issei Sato
Masashi Sugiyama
29
12
0
04 Feb 2019
How does Disagreement Help Generalization against Label Corruption?
How does Disagreement Help Generalization against Label Corruption?
Xingrui Yu
Bo Han
Jiangchao Yao
Gang Niu
Ivor W. Tsang
Masashi Sugiyama
NoLa
76
787
0
14 Jan 2019
Complementary-Label Learning for Arbitrary Losses and Models
Complementary-Label Learning for Arbitrary Losses and Models
Takashi Ishida
Gang Niu
A. Menon
Masashi Sugiyama
VLM
56
113
0
10 Oct 2018
Classification from Positive, Unlabeled and Biased Negative Data
Classification from Positive, Unlabeled and Biased Negative Data
Yu-Guan Hsieh
Gang Niu
Masashi Sugiyama
55
80
0
01 Oct 2018
On the Minimal Supervision for Training Any Binary Classifier from Only
  Unlabeled Data
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
Nan Lu
Gang Niu
A. Menon
Masashi Sugiyama
MQ
73
87
0
31 Aug 2018
Masking: A New Perspective of Noisy Supervision
Masking: A New Perspective of Noisy Supervision
Bo Han
Jiangchao Yao
Gang Niu
Mingyuan Zhou
Ivor Tsang
Ya Zhang
Masashi Sugiyama
NoLa
73
255
0
21 May 2018
Co-teaching: Robust Training of Deep Neural Networks with Extremely
  Noisy Labels
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han
Quanming Yao
Xingrui Yu
Gang Niu
Miao Xu
Weihua Hu
Ivor Tsang
Masashi Sugiyama
NoLa
118
2,078
0
18 Apr 2018
Classification from Pairwise Similarity and Unlabeled Data
Classification from Pairwise Similarity and Unlabeled Data
Han Bao
Gang Niu
Masashi Sugiyama
223
88
0
12 Feb 2018
Learning with Biased Complementary Labels
Learning with Biased Complementary Labels
Xiyu Yu
Tongliang Liu
Biwei Huang
Dacheng Tao
59
198
0
27 Nov 2017
Binary Classification from Positive-Confidence Data
Binary Classification from Positive-Confidence Data
Takashi Ishida
Gang Niu
Masashi Sugiyama
57
57
0
19 Oct 2017
Learning from Complementary Labels
Learning from Complementary Labels
Takashi Ishida
Gang Niu
Weihua Hu
Masashi Sugiyama
53
168
0
22 May 2017
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Tomoya Sakai
Gang Niu
Masashi Sugiyama
56
61
0
04 May 2017
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo
Gang Niu
M. C. D. Plessis
Masashi Sugiyama
71
478
0
02 Mar 2017
Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
HAI
345
4,636
0
10 Nov 2016
Making Deep Neural Networks Robust to Label Noise: a Loss Correction
  Approach
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini
A. Rozza
A. Menon
Richard Nock
Zhuang Li
NoLa
113
1,458
0
13 Sep 2016
Densely Connected Convolutional Networks
Densely Connected Convolutional Networks
Gao Huang
Zhuang Liu
Laurens van der Maaten
Kilian Q. Weinberger
PINN3DV
775
36,861
0
25 Aug 2016
Theoretical Comparisons of Positive-Unlabeled Learning against
  Positive-Negative Learning
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
Gang Niu
M. C. D. Plessis
Tomoya Sakai
Yao Ma
Masashi Sugiyama
69
128
0
10 Mar 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,322
0
10 Dec 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.0K
150,260
0
22 Dec 2014
1