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Pointwise Binary Classification with Pairwise Confidence Comparisons
v1v2v3v4 (latest)

Pointwise Binary Classification with Pairwise Confidence Comparisons

International Conference on Machine Learning (ICML), 2020
5 October 2020
Lei Feng
Senlin Shu
Nan Lu
Bo Han
Miao Xu
Gang Niu
Bo An
Masashi Sugiyama
ArXiv (abs)PDFHTML

Papers citing "Pointwise Binary Classification with Pairwise Confidence Comparisons"

16 / 16 papers shown
Learning from Uncertain Similarity and Unlabeled Data
Learning from Uncertain Similarity and Unlabeled Data
Meng Wei
Zhongnian Li
Peng Ying
Xinzheng Xu
148
0
0
15 Sep 2025
Learning from Similarity-Confidence and Confidence-Difference
Learning from Similarity-Confidence and Confidence-Difference
Tomoya Tate
Kosuke Sugiyama
Masato Uchida
96
0
0
07 Aug 2025
A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision
A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision
Shuying Huang
Junpeng Li
Changchun Hua
Yana Yang
207
0
0
10 Jul 2025
Learning from M-Tuple Dominant Positive and Unlabeled Data
Learning from M-Tuple Dominant Positive and Unlabeled Data
Jiahe Qin
Junpeng Li
Changchun Hua
Yana Yang
182
0
0
25 May 2025
Reduction of Supervision for Biomedical Knowledge Discovery
Reduction of Supervision for Biomedical Knowledge DiscoveryBMC Bioinformatics (BMC Bioinformatics), 2025
Christos Theodoropoulos
Andrei Catalin Coman
James Henderson
Marie-Francine Moens
209
0
0
13 Apr 2025
A General Framework for Learning from Weak Supervision
A General Framework for Learning from Weak Supervision
Hao Chen
Yongfeng Zhang
Lei Feng
Xiang Li
Yidong Wang
Xing Xie
Masashi Sugiyama
Rita Singh
Bhiksha Raj
334
8
0
02 Feb 2024
Binary Classification with Confidence Difference
Binary Classification with Confidence DifferenceNeural Information Processing Systems (NeurIPS), 2023
Wei Wang
Lei Feng
Yuchen Jiang
Gang Niu
Min Zhang
Masashi Sugiyama
203
12
0
09 Oct 2023
Unified Risk Analysis for Weakly Supervised Learning
Unified Risk Analysis for Weakly Supervised Learning
Chao-Kai Chiang
Masashi Sugiyama
270
7
0
15 Sep 2023
A Universal Unbiased Method for Classification from Aggregate
  Observations
A Universal Unbiased Method for Classification from Aggregate ObservationsInternational Conference on Machine Learning (ICML), 2023
Zixi Wei
Lei Feng
Bo Han
Tongliang Liu
Gang Niu
Xiaofeng Zhu
Mengqi Li
246
5
0
20 Jun 2023
A Generalized Unbiased Risk Estimator for Learning with Augmented
  Classes
A Generalized Unbiased Risk Estimator for Learning with Augmented ClassesAAAI Conference on Artificial Intelligence (AAAI), 2023
Senlin Shu
Shuo He
Haobo Wang
Jianguo Huang
Tao Xiang
Lei Feng
165
4
0
12 Jun 2023
Weakly Supervised AUC Optimization: A Unified Partial AUC Approach
Weakly Supervised AUC Optimization: A Unified Partial AUC ApproachIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
Zheng Xie
Yu Liu
Hao He
Ming Li
Zhi Zhou
NoLa
267
12
0
23 May 2023
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 ConfigurationsNeural Information Processing Systems (NeurIPS), 2023
Hao Chen
Ankit Shah
Yongfeng Zhang
R. Tao
Yidong Wang
Xingxu Xie
Masashi Sugiyama
Rita Singh
Bhiksha Raj
284
16
0
22 May 2023
Arch-Graph: Acyclic Architecture Relation Predictor for
  Task-Transferable Neural Architecture Search
Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture SearchComputer Vision and Pattern Recognition (CVPR), 2022
Minbin Huang
Zhijian Huang
Changlin Li
Xin Chen
Hangyang Xu
Zhenguo Li
Xiaodan Liang
245
25
0
12 Apr 2022
Learning with Proper Partial Labels
Learning with Proper Partial LabelsNeural Computation (Neural Comput.), 2021
Zheng Wu
Jiaqi Lv
Masashi Sugiyama
214
11
0
23 Dec 2021
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set
  Classification
Binary Classification from Multiple Unlabeled Datasets via Surrogate Set ClassificationInternational Conference on Machine Learning (ICML), 2021
Nan Lu
Shida Lei
Gang Niu
Issei Sato
Masashi Sugiyama
260
16
0
01 Feb 2021
Combating noisy labels by agreement: A joint training method with
  co-regularization
Combating noisy labels by agreement: A joint training method with co-regularizationComputer Vision and Pattern Recognition (CVPR), 2020
Jianguo Huang
Lei Feng
Xiangyu Chen
Bo An
NoLa
955
632
0
05 Mar 2020
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