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Principled analytic classifier for positive-unlabeled learning via
  weighted integral probability metric

Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric

28 January 2019
Yongchan Kwon
Wonyoung Hedge Kim
Masashi Sugiyama
M. Paik
ArXivPDFHTML

Papers citing "Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric"

15 / 15 papers shown
Title
BOCK : Bayesian Optimization with Cylindrical Kernels
BOCK : Bayesian Optimization with Cylindrical Kernels
Changyong Oh
E. Gavves
Max Welling
56
136
0
05 Jun 2018
Wasserstein Auto-Encoders
Wasserstein Auto-Encoders
Ilya O. Tolstikhin
Olivier Bousquet
Sylvain Gelly
B. Schölkopf
DRL
122
1,057
0
05 Nov 2017
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for
  Unsupervised Domain Adaptation
Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
Hongliang Yan
Yukang Ding
P. Li
Qilong Wang
Yong-mei Xu
W. Zuo
91
574
0
01 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
476
0
02 Mar 2017
Class-prior Estimation for Learning from Positive and Unlabeled Data
Class-prior Estimation for Learning from Positive and Unlabeled Data
M. C. D. Plessis
Gang Niu
Masashi Sugiyama
76
160
0
05 Nov 2016
Efficient Training for Positive Unlabeled Learning
Efficient Training for Positive Unlabeled Learning
Emanuele Sansone
F. D. De Natale
Zhi Zhou
49
67
0
24 Aug 2016
Estimating the class prior and posterior from noisy positives and
  unlabeled data
Estimating the class prior and posterior from noisy positives and unlabeled data
Shantanu Jain
Martha White
P. Radivojac
NoLa
52
122
0
28 Jun 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
127
0
10 Mar 2016
Mixture Proportion Estimation via Kernel Embedding of Distributions
Mixture Proportion Estimation via Kernel Embedding of Distributions
H. G. Ramaswamy
Clayton Scott
Ambuj Tewari
56
198
0
08 Mar 2016
Loss factorization, weakly supervised learning and label noise
  robustness
Loss factorization, weakly supervised learning and label noise robustness
Giorgio Patrini
Frank Nielsen
Richard Nock
M. Carioni
NoLa
155
113
0
08 Feb 2016
Classification with Asymmetric Label Noise: Consistency and Maximal
  Denoising
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
Gilles Blanchard
Marek Flaska
G. Handy
Sara Pozzi
Clayton Scott
NoLa
91
244
0
05 Mar 2013
Agnostic Learning of Monomials by Halfspaces is Hard
Agnostic Learning of Monomials by Halfspaces is Hard
Vitaly Feldman
V. Guruswami
P. Raghavendra
Yi Wu
96
157
0
03 Dec 2010
Universality, Characteristic Kernels and RKHS Embedding of Measures
Universality, Characteristic Kernels and RKHS Embedding of Measures
Bharath K. Sriperumbudur
Kenji Fukumizu
Gert R. G. Lanckriet
224
530
0
03 Mar 2010
Hilbert space embeddings and metrics on probability measures
Hilbert space embeddings and metrics on probability measures
Bharath K. Sriperumbudur
Arthur Gretton
Kenji Fukumizu
Bernhard Schölkopf
Gert R. G. Lanckriet
217
745
0
30 Jul 2009
Fast learning rates for plug-in classifiers
Fast learning rates for plug-in classifiers
Jean-Yves Audibert
Alexandre B. Tsybakov
569
465
0
17 Aug 2007
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