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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

31 August 2018
Nan Lu
Gang Niu
A. Menon
Masashi Sugiyama
    MQ
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Papers citing "On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data"

16 / 16 papers shown
Title
Nearly Optimal Sample Complexity for Learning with Label Proportions
Nearly Optimal Sample Complexity for Learning with Label Proportions
R. Busa-Fekete
Travis Dick
Claudio Gentile
Haim Kaplan
Tomer Koren
Uri Stemmer
43
0
0
08 May 2025
Imprecise Label Learning: A Unified Framework for Learning with Various
  Imprecise Label Configurations
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
Hao Chen
Ankit Shah
Jindong Wang
R. Tao
Yidong Wang
Xingxu Xie
Masashi Sugiyama
Rita Singh
Bhiksha Raj
23
12
0
22 May 2023
Reduction from Complementary-Label Learning to Probability Estimates
Reduction from Complementary-Label Learning to Probability Estimates
Weipeng Lin
Hsuan-Tien Lin
20
9
0
20 Sep 2022
Decompositional Generation Process for Instance-Dependent Partial Label
  Learning
Decompositional Generation Process for Instance-Dependent Partial Label Learning
Congyu Qiao
Ning Xu
Xin Geng
112
75
0
08 Apr 2022
Learning with Proper Partial Labels
Learning with Proper Partial Labels
Zheng Wu
Jiaqi Lv
Masashi Sugiyama
22
8
0
23 Dec 2021
CCMN: A General Framework for Learning with Class-Conditional
  Multi-Label Noise
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise
Ming-Kun Xie
Sheng-Jun Huang
NoLa
16
25
0
16 May 2021
A Survey of Label-noise Representation Learning: Past, Present and
  Future
A Survey of Label-noise Representation Learning: Past, Present and Future
Bo Han
Quanming Yao
Tongliang Liu
Gang Niu
Ivor W. Tsang
James T. Kwok
Masashi Sugiyama
NoLa
24
158
0
09 Nov 2020
When Optimizing $f$-divergence is Robust with Label Noise
When Optimizing fff-divergence is Robust with Label Noise
Jiaheng Wei
Yang Liu
24
54
0
07 Nov 2020
Pointwise Binary Classification with Pairwise Confidence Comparisons
Pointwise Binary Classification with Pairwise Confidence Comparisons
Lei Feng
Senlin Shu
Nan Lu
Bo Han
Miao Xu
Gang Niu
Bo An
Masashi Sugiyama
23
21
0
05 Oct 2020
Rethinking Importance Weighting for Deep Learning under Distribution
  Shift
Rethinking Importance Weighting for Deep Learning under Distribution Shift
Tongtong Fang
Nan Lu
Gang Niu
Masashi Sugiyama
25
133
0
08 Jun 2020
Combating noisy labels by agreement: A joint training method with
  co-regularization
Combating noisy labels by agreement: A joint training method with co-regularization
Hongxin Wei
Lei Feng
Xiangyu Chen
Bo An
NoLa
303
497
0
05 Mar 2020
Progressive Identification of True Labels for Partial-Label Learning
Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv
Miao Xu
Lei Feng
Gang Niu
Xin Geng
Masashi Sugiyama
11
177
0
19 Feb 2020
Learning with Multiple Complementary Labels
Learning with Multiple Complementary Labels
Lei Feng
Takuo Kaneko
Bo Han
Gang Niu
Bo An
Masashi Sugiyama
13
92
0
30 Dec 2019
Classification from Pairwise Similarity and Unlabeled Data
Classification from Pairwise Similarity and Unlabeled Data
Han Bao
Gang Niu
Masashi Sugiyama
165
87
0
12 Feb 2018
Binary Classification from Positive-Confidence Data
Binary Classification from Positive-Confidence Data
Takashi Ishida
Gang Niu
Masashi Sugiyama
20
56
0
19 Oct 2017
Learning from Binary Labels with Instance-Dependent Corruption
Learning from Binary Labels with Instance-Dependent Corruption
A. Menon
Brendan van Rooyen
Nagarajan Natarajan
NoLa
31
41
0
03 May 2016
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