Committee 
Date Time 
Place 
Paper Title / Authors 
Abstract 
Paper # 
NC, MBE (Joint) 
20220930 10:30 
Miyagi 
Tohoku Univ. (Primary: Onsite, Secondary: Online) 
An approximation algorithm for computing integrated information based on Gaussian process regression Tadaaki Hosaka (TUS) NC202240 
The framework of Integrated Information Theory, which aims to mathematically deal with consciousness, consists of multip... [more] 
NC202240 pp.3237 
IT 
20220722 13:50 
Okayama 
Okayama University of Science (Primary: Onsite, Secondary: Online) 
An Efficient Algorithm for Optimal Decision on Piecewise Linear Regression Model by Bayes Decision Theory Noboru Namegaya, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT202225 
In this study, we propose a Beyesoptimal prediction method on a piecewise linear regression model by Bayes decision the... [more] 
IT202225 pp.5155 
IBISML 
20210303 14:25 
Online 
Online 
Markov Decision Processes for Simultaneous Control of Multiple Objects with Different State Transition Probabilities in Each Cluster Yuto Motomura, Akira Kamatsuka, Koki Kazama, Toshiyasu Matsushima (Waseda Univ.) IBISML202049 
In this study, we propose an extended MDP model, which is a Markov decision process model with multiple control objects ... [more] 
IBISML202049 pp.4754 
IT, EMM 
20200528 15:00 
Online 
Online 
Bayes Optimal Detecting Relevant Changes for i.p.i.d. Sources Kairi Suzuki, Akira Kamatsuka, Toshiyasu Matsushima (Waseda Univ.) IT20203 EMM20203 
The problems of detecting change points are studied in various fields.
There are various types of changepoint detectio... [more] 
IT20203 EMM20203 pp.1318 
ISEC, IT, WBS 
20200310 13:50 
Hyogo 
University of Hyogo (Cancelled but technical report was issued) 
A Statistical DecisionTheoretic Approach for Measuring Privacy Risk in Information Disclosure Problem Alisa Miyashita, Akira Kamatsuka (Waseda Univ.), Takahiro Yoshida (Yokohama College of Commerce), Toshiyasu Matsushima (Waseda Univ.) IT2019104 ISEC2019100 WBS201953 
In this paper, we deal with the problem of database statistics publishing with privacy and utility guarantees. While var... [more] 
IT2019104 ISEC2019100 WBS201953 pp.95100 
NC, MBE (Joint) 
20200305 13:00 
Tokyo 
University of Electro Communications (Cancelled but technical report was issued) 
Bayesian learning curve for the case when the optimal distribution is not unique Shuya Nagayasu, Sumio Watanabe (Tokyo Tech) NC201994 
Bayesian inference is a widely used statistical method. Asymptotic behaviors of generalization loss and free energy in B... [more] 
NC201994 pp.107112 
ISEC, SITE, ICSS, EMM, HWS, BioX, IPSJCSEC, IPSJSPT [detail] 
20190724 10:55 
Kochi 
Kochi University of Technology 
Stochastic Existence Connecting Logos that are not necessarily completely divided and Language Games
 Limitations of Security Models and the Possibility of Artificial Intelligence  Tetsuya Morizumi (KU) ISEC201949 SITE201943 BioX201941 HWS201944 ICSS201947 EMM201952 
In this paper we describe that AI architecture including input data in artificial intelligence system for Bayesian estim... [more] 
ISEC201949 SITE201943 BioX201941 HWS201944 ICSS201947 EMM201952 pp.317324 
EA 
20181214 10:30 
Fukuoka 
kyushu Univ. 
Bayesian Filter for Sound Environment by Considering Additive Property of Energy Variable and Fuzzy Observation in Decibel Scale
 Introduction of Fuzzy Moments  Akira Ikuta, Hisako Orimoto (Prefectural Univ. Hiroshima) EA201889 
In the measurement and evaluation of actual random signal in a sound environment, the observed data often contain the fu... [more] 
EA201889 pp.5158 
IBISML 
20181105 15:10 
Hokkaido 
Hokkaido Citizens Activites Center (Kaderu 2.7) 
[Poster Presentation]
Active learning for identifying local minimum points based on the derivative of Gaussian process Yu Inatsu (RIKEN), Daisuke Sugita (NITech), Kazuaki Toyoura (Kyoto Univ.), Ichiro Takeuchi (NITech/RIKEN/NIMS) IBISML201894 
In many fields such as materials science, knowing local minimum points of unknown functions is important for understand... [more] 
IBISML201894 pp.373380 
IBISML 
20181105 15:10 
Hokkaido 
Hokkaido Citizens Activites Center (Kaderu 2.7) 
[Poster Presentation]
A Note on the Estimation Method of Causality Effects based on Statistical Decision Theory Shunsuke Horii, Tota Suko (Waseda Univ.) IBISML201897 
In this paper, we deal with the problem of estimating the intervention effect in statistical causal analysis using struc... [more] 
IBISML201897 pp.397402 
NS 
20181019 10:40 
Kyoto 
Kyoto Kyoiku Bunka Center 
[Invited Lecture]
Study of Internet Traffic Classification Based on Naive Bayesian Method Yuanzhi Shao (UEC), Ved P. Kafle (NICT) NS2018127 
The traditional port and payloadbased traffic detection and classification technique may not meet the security and QoS... [more] 
NS2018127 pp.111116 
MSS 
20170316 16:50 
Shimane 
Shimane Univ. 
Finding the Candidates of Attributes in Mail Words Filtered by Bayesian Method Nozomi Fujii, Manabu Sugii, Hiroshi Matsuno (Yamacguchi Univ.) MSS201689 
The interest of a research about mailfiltering system is in a method based on Bayesian theory．The existing systems for ... [more] 
MSS201689 pp.4348 
IT, ISEC, WBS 
20160310 11:05 
Tokyo 
The University of ElectroCommunications 
Bayesian Estimation of the Virus Source of an Infection in a Network Using Any Prior Distribution Ryousuke Kido, Tetsunao Matsuta, Ryutaroh Matsumoto, Tomohiko Uyematsu (Tokyo Tech.) IT2015104 ISEC201563 WBS201587 
We model a phenomenon that a computer virus is spreading in a network as infection of the virus in a graph constructed o... [more] 
IT2015104 ISEC201563 WBS201587 pp.1924 
MBE, NC (Joint) 
20141122 13:40 
Miyagi 
Tohoku University 
A Bayesian Network Model of theoryofmind Naoya Nishio, Nobuhiko Asakura, Toshio Inui (Kyoro Univ.) NC201439 
Theory of mind (ToM) is a knowledge and cognitive framework for understanding the minds of others and predicting their a... [more] 
NC201439 pp.7378 
PRMU, IBISML, IPSJCVIM [detail] 
20130902 13:00 
Tottori 

[Invited Talk]
Topics on the Cost in Machine Learning Shotaro Akaho (AIST) PRMU201340 IBISML201320 
Most machine learning algorithms minimize some cost functions, therefore
the cost is a general target of research.
... [more] 
PRMU201340 IBISML201320 pp.4748 
NC 
20120731 11:35 
Shiga 
Ritsumeikan Univ. College of Information Science and Engineering 
A Biological Implementation of Bayesian Estimation Algorithm in a Neural Network Architecture Based on Discrete Choice Theory Daiki Futagi, Ryota Kobayashi, Katsunori Kitano (Ritsumeikan Univ) NC201228 
Bayesian estimation theory has been expected to explain how brain deals with uncertainty such as feature extraction agai... [more] 
NC201228 pp.7782 
IBISML 
20111110 15:45 
Nara 
Nara Womens Univ. 
Sequential Network Change Detection with Its Applications to Advertisement Impact Relation Analysis Yu Hayashi, Kenji Yamanishi (Univ. of Tokyo.) IBISML201171 
This paper addresses the issue of network change detection from nonstationary time series data. We employ as a represen... [more] 
IBISML201171 pp.199206 
IBISML 
20110620 14:30 
Tokyo 
Takeda Hall 
Network Change Detection with Its Applications to Advertisement Impact Relation Analysis Yu Hayashi, Kenji Yamanishi (Univ. of Tokyo.) IBISML20119 
This paper addresses the issue of network change detection with its applications to advertisement impact relation analys... [more] 
IBISML20119 pp.5966 
HCGSYMPO (2nd) 
20101215  20101217 
Miyazaki 
Miyazaki Seagai resort 
Analysis of Psychological Stress Factors with Spontaneous Facial Expressions using Bayesian Networks Hiroaki Otsu, Kazuhito Sato, Hirokazu Madokoro (Akita Prefectural Univ.), Sakura Kadowaki (SmartDesign) 
This paper presents a method to create an individual model to describe relations between facial expressions and stress p... [more] 

NC 
20081023 10:15 
Miyagi 
Tohoku Univ. 
Statistical Mechanical Approach for Computational Neuroscience
 With the use of the Primary Visual Area Model  Ken Takiyama (Tokyo Univ.), Yasushi Naruse (NICT), Masato Okada (Tokyo Univ./RIKEN BSI) NC200839 
In this study, we propose a multihypercolumn model consisting of $K$ hypercolumns. Adjacent hypercolumns have interhyp... [more] 
NC200839 pp.1924 