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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2022-12-23
Kyoto Kyoto University
(Primary: On-site, Secondary: Online)
Enhancement of Audio Signals Using Learning from Positive and Unlabelled Data
Nobutaka Ito, Masashi Sugiyama (UTokyo) IBISML2022-56
Audio signal enhancement (SE) is the task of extracting a desired class of sounds (a “signal”) from an observed sound mi... [more] IBISML2022-56
IBISML 2022-03-08
Online Online Robust computation of optimal transport by β-potential regularization
Shintaro Nakamura (Univ. Tokyo), Han Bao (Univ.Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. Tokyo) IBISML2021-31
Optimal transport (OT) has become a widely used tool to measure the discrepancy between probability distributions
in th... [more]
IBISML 2022-03-09
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) IBISML2021-44
 [more] IBISML2021-44
IBISML 2021-03-02
Online Online Learning from Noisy Complementary Labels with Robust Loss Functions
Hiroki Ishiguro (UTokyo), Takashi Ishida (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2020-34
It has been demonstrated that large-scale labeled datasets facilitate the success of machine learning. However, collecti... [more] IBISML2020-34
IBISML 2020-03-11
Kyoto Kyoto University
(Cancelled but technical report was issued)
Calibrated Surrogate Maximization of Linear-Fractional Utility in Binary Classification
Han Bao (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2019-43
Complex classification performance metrics such as the F-measure and Jaccard index are often used to handle class imbala... [more] IBISML2019-43
IBISML 2017-11-09
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
IBISML 2017-11-10
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
IBISML 2017-11-10
Tokyo Univ. of Tokyo [Poster Presentation] Multi-Task Learning with Positive and Unlabeled Data and Its Application to Mental State Prediction
Hirotaka Kaji, Hayato Yamaguchi (Toyota Motor), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-66
In real-world machine learning applications, we are often faced with a situation where only a small number of training s... [more] IBISML2017-66
IBISML 2017-11-10
Tokyo Univ. of Tokyo Hierarchical Reinforcement Learning Based on Return-Weighted Density Estimation
Takayuki Osa (UTokyo/RIKEN), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-67
We propose a hierarchical reinforcement learning (HRL) methods for learning the optimal policy from a multi-modal reward... [more] IBISML2017-67
IBISML 2017-11-10
Tokyo Univ. of Tokyo Good Arm Identification via Bandit Feedback
Hideaki Kano, Junya Honda (UTokyo/RIKEN), Kentaro Sakamaki (UTokyo), Kentaro Matsuura (Johnson & Johnson), Atsuyoshi Nakamura (HU), Masashi Sugiyama (RIKEN/UTokyo) IBISML2017-82
In this paper, we consider and discuss a new stochastic multi-armed bandit problem called {em good arm identification} (... [more] IBISML2017-82
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-24
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
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-24
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
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-24
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
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-25
Okinawa Okinawa Institute of Science and Technology Expectation Propagation for t-Exponential Family
Futoshi Futami, Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-6
Exponential family distributions are highly useful in machine learning since their calculation can be performed efficien... [more] IBISML2017-6
NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] 2017-06-25
Okinawa Okinawa Institute of Science and Technology Stochastic Divergence Minimization for Biterm Topic Model
Zhenghang Cui (Univ. of Tokyo), Issei Sato (Univ. of Tokyo/RIKEN), Masashi Sugiyama (RIKEN/Univ. of Tokyo) IBISML2017-7
Inferring latent topics of collected short texts is useful for understanding its hidden structure and predicting new con... [more] IBISML2017-7
IBISML 2016-11-16
Kyoto Kyoto Univ. Structural Change Detection in Lithography Systems
Yosuke Otsubo (NIKON), Masashi Sugiyama (RIKEN/UTokyo) IBISML2016-45
A lithography system, which consists of various mechanical units such as high-precision optical systems, prints circuit ... [more] IBISML2016-45
IBISML 2016-11-16
Kyoto Kyoto Univ. Robust supervised learning under uncertainty in dataset shift
Weihua Hu, Issei Sato (UTokyo), Masashi Sugiyama (RIKEN/UTokyo) IBISML2016-50
When machine learning is deployed in the real world, its performance can be significantly undermined because test data m... [more] IBISML2016-50
IBISML 2016-11-16
Kyoto Kyoto Univ. Policy Search with High-dimensional Context Variables
Voot Tangkaratt (The Univ. of Tokyo), Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters (Technical Univ. of Darmstadt), Masashi Sugiyama (The Univ. of Tokyo) IBISML2016-63
 [more] IBISML2016-63
IBISML 2016-11-17
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
IBISML 2016-03-17
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
 Results 1 - 20 of 105  /  [Next]  
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