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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 14 of 14  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2022-03-09
13:30
Online Online Is the Performance of My Deep Network Too Good to Be True? -- A Direct Approach to Estimating the Bayes Error in Binary Classification --
Takashi Ishida (UTokyo), Ikko Yamane (Université Paris Dauphine-PSL/RIKEN), Nontawat Charoenphakdee (UTokyo), Gang Niu (RIKEN), Masashi Sugiyama (RIKEN/UTokyo)
 [more] IBISML2021-44
pp.38-45
IBISML 2017-11-09
13:00
Tokyo Univ. of Tokyo Semi-Supervised AUC Optimization based on Positive-Unlabeled Learning
Tomoya Sakai, Gang Niu (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-40
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced classif... [more] IBISML2017-40
pp.39-46
IBISML 2017-11-10
13:00
Tokyo Univ. of Tokyo [Poster Presentation] Binary Classification from Positive-Confidence Data
Takashi Ishida (SMAM/UTokyo/RIKEN), Gang Niu (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-62
Reducing labeling costs in supervised learning is a critical issue in many practical machine learning applications. In ... [more] IBISML2017-62
pp.207-214
PRMU, IBISML, IPSJ-CVIM [detail] 2017-09-16
13:00
Tokyo   [Invited Talk] Recent Advances on Positive-Unlabeled (PU) Learning
Gang Niu (UTokyo)
 [more]
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-24
10:45
Okinawa Okinawa Institute of Science and Technology Positive-Unlabeled Learning with Non-Negative Risk Estimator
Ryuichi Kiryo (Univ. of Tokyo/RIKEN), Gang Niu (Univ. of Tokyo), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-4
From only emph{positive}~(P) and emph{unlabeled}~(U) data, a binary classifier can be trained with PU learning, in which... [more] IBISML2017-4
pp.63-70
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-24
11:10
Okinawa Okinawa Institute of Science and Technology Learning from Complementary Labels
Takashi Ishida (SMAM/Univ. of Tokyo), Gang Niu (Univ. of Tokyo), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-5
 [more] IBISML2017-5
pp.71-78
IBISML 2016-11-17
14:00
Kyoto Kyoto Univ. Semi-Supervised Classification Based on Classification from Positive and Unlabeled Data
Tomoya Sakai, Marthinus Christoffel du Plessis, Gang Niu (UTokyo), Masashi Sugiyama (RIKEN/UTokyo) IBISML2016-80
Most of the semi-supervised learning methods developed so far use unlabeled data for regularization purposes under parti... [more] IBISML2016-80
pp.243-250
IBISML 2015-11-26
15:00
Ibaraki Epochal Tsukuba [Poster Presentation] Regularized Policy Gradients -- Direct Variance Reduction in Policy Gradient Estimation --
Tingting Zhao (TUST), Gang Niu (UTokyo), Ning Xie (Tongji Univ), Jucheng Yang (TUST), Masashi Sugiyama (UTokyo) IBISML2015-68
 [more] IBISML2015-68
pp.115-122
IBISML 2015-11-27
14:00
Ibaraki Epochal Tsukuba [Poster Presentation] Non-Gaussian Component Analysis with Log-Density-Gradient Estimation
Hiroaki Sasaki, Gang Niu, Masashi Sugiyama (UTokyo) IBISML2015-82
 [more] IBISML2015-82
pp.217-224
IBISML 2014-11-18
15:00
Aichi Nagoya Univ. [Poster Presentation] Learning from Positive and Unlabeled Data 1: Classifier Training and Theoretical Analysis
Marthinus Christoffel du Plessis (Univ. of Tokyo), Gang Niu (Baidu), Masashi Sugiyama (Univ. of Tokyo) IBISML2014-65
(Advance abstract in Japanese is available) [more] IBISML2014-65
pp.227-233
IBISML 2014-11-18
15:00
Aichi Nagoya Univ. [Poster Presentation] Learning from Positive and Unlabeled Data 2: Computationally Efficient Estimation of Class Priors
Marthinus Christoffel du Plessis (Univ. of Tokyo), Gang Niu (Baidu), Masashi Sugiyama (Univ. of Tokyo) IBISML2014-66
(Advance abstract in Japanese is available) [more] IBISML2014-66
pp.235-241
IBISML 2012-03-13
16:55
Tokyo The Institute of Statistical Mathematics Squared-loss Mutual Information Regularization
Gang Niu, Wittawat Jitkrittum, Hirotaka Hachiya (Tokyo Inst. of Tech.), Bo Dai (Purdue Univ.), Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2011-108
The information maximization principle is a useful alternative to the low-density separation principle and prefers proba... [more] IBISML2011-108
pp.147-153
IBISML 2011-06-20
11:05
Tokyo Takeda Hall SERAPH: Semi-supervised Metric Learning Paradigm with Hyper Sparsity
Gang Niu (Tokyo Inst. of Tech.), Bo Dai (Chinese Academy Of Sciences), Makoto Yamada, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2011-8
We consider the problem of learning a distance metric from very limited side information with unlabeled data. The propos... [more] IBISML2011-8
pp.51-58
IBISML 2011-06-21
14:15
Tokyo Takeda Hall Analysis and Improvement of Policy Gradient Estimation
Tingting Zhao, Hirotaka Hachiya, Gang Niu, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2011-12
Policy gradient is a useful model-free reinforcement learning approach,
but it tends to suffer from instability of grad... [more]
IBISML2011-12
pp.83-89
 Results 1 - 14 of 14  /   
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