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Committee Date Time Place Paper Title / Authors Abstract Paper #
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
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-24
10:20		 Okinawa Okinawa Institute of Science and Technology Risk Minimization Framework for Multiple Instance Learning from Positive and Unlabeled Bags
Han Bao (Univ. of Tokyo), Tomoya Sakai, Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-3		 Multiple instance learning (MIL) is a variation of traditional supervised learning problems where data (referred to as b... [more] IBISML2017-3
pp.55-62
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 2016-03-17
13:50		 Tokyo Institute of Statistical Mathematics Least-Squares Log-Density Gradient Clustering for Riemannian Manifolds
Mina Ashizawa (UTokyo), Hiroaki Sasaki (NAIST), Tomoya Sakai, Masashi Sugiyama (UTokyo) IBISML2015-96		  [more] IBISML2015-96
pp.17-24
IBISML 2013-11-13
15:45		 Tokyo Tokyo Institute of Technology, Kuramae-Kaikan [Poster Presentation] Computationally Efficient Estimation of Squared-loss Mutual Information with Multiplicative Kernel Models
Tomoya Sakai, Masashi Sugiyama (Tokyo Inst. of Tech.) IBISML2013-53		 emph{Squared-loss mutual information} (SMI) is a robust measure of statistical dependence between random variables.
The... [more]		 IBISML2013-53
pp.131-137
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