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1602.02450
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Loss factorization, weakly supervised learning and label noise robustness
8 February 2016
Giorgio Patrini
Frank Nielsen
Richard Nock
M. Carioni
NoLa
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Papers citing
"Loss factorization, weakly supervised learning and label noise robustness"
50 / 53 papers shown
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CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection
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Zhen Tan
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Zongsheng Cao
Yu Kong
Huan Liu
312
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20 Nov 2023
Towards Data-centric Graph Machine Learning: Review and Outlook
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Yixin Liu
Zhifeng Bao
Meng Fang
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20 Sep 2023
Towards Label-free Scene Understanding by Vision Foundation Models
Neural Information Processing Systems (NeurIPS), 2023
Runnan Chen
You-Chen Liu
Lingdong Kong
Nenglun Chen
Xinge Zhu
Yuexin Ma
Tongliang Liu
Wenping Wang
VLM
259
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0
06 Jun 2023
Robust Nonparametric Regression under Poisoning Attack
AAAI Conference on Artificial Intelligence (AAAI), 2023
Puning Zhao
Z. Wan
AAML
259
12
0
26 May 2023
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
IEEE Transactions on Artificial Intelligence (IEEE TAI), 2022
Nabeel Seedat
F. Imrie
M. Schaar
326
19
0
09 Nov 2022
Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees
M. Ponti
L. Oliveira
Mathias Esteban
Valentina Garcia
J. Román
Luis Argerich
TDI
229
5
0
20 Oct 2022
Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels
Machine-mediated learning (ML), 2022
Chuang Zhang
Li Shen
Jian Yang
Chen Gong
NoLa
233
5
0
27 Jun 2022
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation
Computer Vision and Pattern Recognition (CVPR), 2022
De Cheng
Tongliang Liu
Yixiong Ning
Nannan Wang
Bo Han
Gang Niu
Xinbo Gao
Masashi Sugiyama
NoLa
274
87
0
06 Jun 2022
Bias-Variance Decompositions for Margin Losses
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Danny Wood
Tingting Mu
Gavin Brown
UQCV
261
7
0
26 Apr 2022
Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation
SDM (SDM), 2022
Yongliang Ding
Tao Zhou
Chuang Zhang
Yijing Luo
Juan Tang
Chen Gong
NoLa
262
4
0
21 Mar 2022
Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
Seong Min Kye
Kwanghee Choi
Joonyoung Yi
Buru Chang
NoLa
394
27
0
29 Nov 2021
Binary classification with corrupted labels
Yonghoon Lee
Rina Foygel Barber
401
11
0
16 Jun 2021
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2021
Aritra Ghosh
Andrew Lan
NoLa
275
12
0
19 Apr 2021
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Curtis G. Northcutt
Anish Athalye
Jonas W. Mueller
537
662
0
26 Mar 2021
Exploiting Class Similarity for Machine Learning with Confidence Labels and Projective Loss Functions
Jana Armouti
J. Galeotti
190
3
0
25 Mar 2021
Analysing the Noise Model Error for Realistic Noisy Label Data
AAAI Conference on Artificial Intelligence (AAAI), 2021
Michael A. Hedderich
D. Zhu
Dietrich Klakow
NoLa
326
25
0
24 Jan 2021
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
AAAI Conference on Artificial Intelligence (AAAI), 2020
Pengfei Chen
Junjie Ye
Guangyong Chen
Jingwei Zhao
Pheng-Ann Heng
NoLa
322
153
0
10 Dec 2020
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
493
186
0
09 Nov 2020
Learning from Noisy Labels with Deep Neural Networks: A Survey
IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS), 2020
Hwanjun Song
Minseok Kim
Dongmin Park
Yooju Shin
Jae-Gil Lee
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1.4K
1,294
0
16 Jul 2020
Calibrated Surrogate Losses for Adversarially Robust Classification
Annual Conference Computational Learning Theory (COLT), 2020
Han Bao
Clayton Scott
Masashi Sugiyama
282
47
0
28 May 2020
Does label smoothing mitigate label noise?
International Conference on Machine Learning (ICML), 2020
Michal Lukasik
Srinadh Bhojanapalli
A. Menon
Surinder Kumar
NoLa
405
408
0
05 Mar 2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible
International Conference on Machine Learning (ICML), 2019
Antonin Berthon
Bo Han
Gang Niu
Tongliang Liu
Masashi Sugiyama
NoLa
407
124
0
11 Jan 2020
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
Knowledge-Based Systems (KBS), 2019
G. Algan
ilkay Ulusoy
NoLa
VLM
442
367
0
11 Dec 2019
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis
Davood Karimi
Haoran Dou
Simon K. Warfield
Ali Gholipour
NoLa
444
650
0
05 Dec 2019
Positive-Unlabeled Reward Learning
Conference on Robot Learning (CoRL), 2019
Danfei Xu
Misha Denil
286
45
0
01 Nov 2019
Confident Learning: Estimating Uncertainty in Dataset Labels
Journal of Artificial Intelligence Research (JAIR), 2019
Curtis G. Northcutt
Lu Jiang
Isaac L. Chuang
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904
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31 Oct 2019
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
Neural Information Processing Systems (NeurIPS), 2019
Xinyang Yi
Zhaoran Wang
Zhuoran Yang
Constantine Caramanis
Han Liu
267
6
0
14 Jul 2019
Learning Graph Neural Networks with Noisy Labels
Hoang NT
C. J. Jin
T. Murata
NoLa
174
57
0
05 May 2019
Classification from Pairwise Similarities/Dissimilarities and Unlabeled Data via Empirical Risk Minimization
Takuya Shimada
Han Bao
Issei Sato
Masashi Sugiyama
167
49
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26 Apr 2019
Learning with Inadequate and Incorrect Supervision
Chen Gong
Hengmin Zhang
Zhiqiang Wang
Dacheng Tao
212
34
0
20 Feb 2019
Semi-Supervised Ordinal Regression Based on Empirical Risk Minimization
Neural Computation (Neural Comput.), 2019
Taira Tsuchiya
Nontawat Charoenphakdee
Issei Sato
Masashi Sugiyama
OffRL
357
4
0
31 Jan 2019
Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric
Yongchan Kwon
Wonyoung Hedge Kim
Masashi Sugiyama
M. Paik
726
8
0
28 Jan 2019
Cost Sensitive Learning in the Presence of Symmetric Label Noise
Sandhya Tripathi
N. Hemachandra
NoLa
950
6
0
08 Jan 2019
Bootstrapping Deep Neural Networks from Approximate Image Processing Pipelines
Kilho Son
Jesse Hostetler
S. Chai
160
0
0
29 Nov 2018
Label Propagation for Learning with Label Proportions
Rafael Poyiadzi
Raúl Santos-Rodríguez
Niall Twomey
126
13
0
24 Oct 2018
Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error
Nontawat Charoenphakdee
Masashi Sugiyama
462
11
0
19 Sep 2018
Classification with imperfect training labels
T. Cannings
Yingying Fan
R. Samworth
379
50
0
29 May 2018
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
Yifan Ding
Liqiang Wang
Deliang Fan
Boqing Gong
NoLa
401
107
0
08 Feb 2018
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
Stephen Hardy
Wilko Henecka
Hamish Ivey-Law
Richard Nock
Giorgio Patrini
Guillaume Smith
Brian Thorne
FedML
253
606
0
29 Nov 2017
Generative Adversarial Positive-Unlabelled Learning
Ming Hou
B. Chaib-draa
Chao Li
Qibin Zhao
205
6
0
21 Nov 2017
Fidelity-Weighted Learning
Mostafa Dehghani
Arash Mehrjou
Stephan Gouws
J. Kamps
Bernhard Schölkopf
NoLa
FedML
232
77
0
08 Nov 2017
Learning with Bounded Instance- and Label-dependent Label Noise
Jiacheng Cheng
Tongliang Liu
K. Ramamohanarao
Dacheng Tao
NoLa
393
160
0
12 Sep 2017
Decoupling "when to update" from "how to update"
Neural Information Processing Systems (NeurIPS), 2017
Eran Malach
Shai Shalev-Shwartz
NoLa
359
639
0
08 Jun 2017
Learning Deep Networks from Noisy Labels with Dropout Regularization
Ishan Jindal
M. Nokleby
Xuewen Chen
NoLa
236
197
0
09 May 2017
Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo
Gang Niu
M. C. D. Plessis
Masashi Sugiyama
464
561
0
02 Mar 2017
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini
A. Rozza
A. Menon
Richard Nock
Zhuang Li
NoLa
554
1,642
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The Crossover Process: Learnability and Data Protection from Inference Attacks
Richard Nock
Giorgio Patrini
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Tibério S. Caetano
209
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