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| 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 |
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