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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IT | 2023-08-04 13:45 |
Kanagawa | Shonan Institute of Technology (Primary: On-site, Secondary: Online) |
A Note on Private Coded Computation Method with Tolerance against Erasure of Responses from Server Groups Yusuke Morishita, Atsushi Miki, Koki Kazama, Toshiyasu Matsushima (Waseda Univ.)  IT2023-27 |
Coded Computation (CC) is used by a user to perform Distributed Computation using multiple servers to speed up the compu... [more] | IT2023-27 pp.74-79 |
| EMM, IT | 2023-05-11 13:50 |
Kyoto | Rakuyu Kaikan (Kyoto Univ. Yoshida-South Campus) (Primary: On-site, Secondary: Online) |
A Note on a New Method for Reducing Computation Time in Private Coded Computation Atsushi Miki, Shenzhe Gao, Koki Kazama, Toshiyasu Matsushima (Waseda Univ.) IT2023-3 EMM2023-3 |
Coded Computation is a mechanism for accelerating computations through distributed processing using multiple servers. Th... [more] | IT2023-3 EMM2023-3 pp.12-17 |
| IT, RCS, SIP | 2023-01-25 14:35 |
Gunma | Maebashi Terrsa (Primary: On-site, Secondary: Online) |
An Optimal Prediction on Multilevel Coefficient Linear Regression Model by Bayes Decision Theory and Its Approximation Method Kohei Horinouchi, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-67 SIP2022-118 RCS2022-246 |
It is common practice to apply Multilevel Analysis for the data sampled from various classes. In this Analysis, it is co... [more] | IT2022-67 SIP2022-118 RCS2022-246 pp.217-222 |
| IT | 2022-07-22 13:50 |
Okayama | Okayama University of Science (Primary: On-site, 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.) IT2022-25 |
In this study, we propose a Beyes-optimal prediction method on a piecewise linear regression model by Bayes decision the... [more] | IT2022-25 pp.51-55 |
| IT | 2022-07-22 14:15 |
Okayama | Okayama University of Science (Primary: On-site, Secondary: Online) |
A Study on Multilevel Coefficient Linear Regression Model and an Optimal Prediction for Multilevel Data by Bayes Decision Theory Kohei Horinouchi, Naoki Ichijo, Taisuke Ishiwatari, Toshiyasu Matsushima (Waseda Univ.) IT2022-26 |
It is common practice to apply Multilevel Model (Linear Mixed Model, Hierarchical Linear Model) for the data sampled fro... [more] | IT2022-26 pp.56-60 |
| IT | 2022-07-22 14:40 |
Okayama | Okayama University of Science (Primary: On-site, Secondary: Online) |
Meta-Tree Set Construction for Approximate Bayes Optimal Prediction on Decision Tree Model Keito Tajima, Naoki Ichijo, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-27 |
Decision trees are generally used as a predictive function, but some studies use decision trees as data-generative model... [more] | IT2022-27 pp.61-66 |
| IT | 2022-07-22 15:05 |
Okayama | Okayama University of Science (Primary: On-site, Secondary: Online) |
Bayes Optimal Approximation Algorithm by Boosting-like Construction of Meta-Tree Sets in Classification on Decision Tree Model Ryota Maniwa, Naoki Ichijo, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-28 |
Decision trees are used for classification and regression such as predicting the objective variable corresponding to the... [more] | IT2022-28 pp.67-72 |
| IT, EMM | 2022-05-17 13:25 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
A Note on Time-Varying Two-Dimensional Autoregressive Models and the Bayes Codes Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2022-2 EMM2022-2 |
This paper proposes a two-dimensional autoregressive model with time-varying parameters as a stochastic model for explai... [more] | IT2022-2 EMM2022-2 pp.7-12 |
| IBISML | 2022-03-08 11:20 |
Online | Online | Tree-Structured Generative Model with Latent Variables and Approximate Variational Bayesian Inference Naoki Ichijo, Yuta Nakahara (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IBISML2021-33 |
[more] | IBISML2021-33 pp.19-26 |
| RCS, SIP, IT | 2022-01-21 09:00 |
Online | Online | An Approximation by Meta-Tree Boosting Method to Bayesian Optimal Prediction for Decision Tree Model Wenbin Yu, Koki Kazama, Yuta Nakahara, Naoki Ichijo (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-67 SIP2021-75 RCS2021-235 |
[more] | IT2021-67 SIP2021-75 RCS2021-235 pp.219-224 |
| IT | 2021-07-09 13:25 |
Online | Online | A Note on the Reduction of Computational Complexity for Linear Regression Model Including Cluster Explanatory Variables and Regression Explanatory Variables
-- Bayes Optimal Prediction and Sub-Optimal Algorithm -- Sho Kayama (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-24 |
By considering the probability model with the structure that the data is divided into clusters and each cluster has an i... [more] | IT2021-24 pp.51-56 |
| WBS, IT, ISEC | 2021-03-04 10:55 |
Online | Online | An Efficient Bayes Coding Algorithm for the Source Based on Context Tree Models that Vary from Section to Section Koshi Shimada, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-115 ISEC2020-45 WBS2020-34 |
In this paper, we present an efficient coding algorithm for a non-stationary source based on context tree models that ve... [more] | IT2020-115 ISEC2020-45 WBS2020-34 pp.19-24 |
| WBS, IT, ISEC | 2021-03-04 13:20 |
Online | Online | [Poster Presentation]
Non-asymptotic converse theorem on the overflow probability of variable-to-fixed length codes Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-130 ISEC2020-60 WBS2020-49 |
This study considers variable-to-fixed length codes and investigates the non-asymptotic converse theorem on the threshol... [more] | IT2020-130 ISEC2020-60 WBS2020-49 pp.115-116 |
| EA, US, SP, SIP, IPSJ-SLP [detail] | 2021-03-03 16:45 |
Online | Online | An optimal prediction of phoneme under Bayes criterion by weighting multiple hidden Markov models Taishi Yamaoka, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) EA2020-76 SIP2020-107 SP2020-41 |
In this paper, we propose a prediction method for prediction problems using a hidden Markov model. Specifically, it is a... [more] | EA2020-76 SIP2020-107 SP2020-41 pp.97-102 |
| IBISML | 2021-03-03 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.) IBISML2020-49 |
In this study, we propose an extended MDP model, which is a Markov decision process model with multiple control objects ... [more] | IBISML2020-49 pp.47-54 |
| SIP, IT, RCS | 2021-01-22 15:15 |
Online | Online | An Image Generative Model with Various Auto-regressive Coefficients Depending on Neighboring Pixels and the Bayes Code for It Masahiro Takano, Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2020-108 SIP2020-86 RCS2020-199 |
In this papar, we propose an expanded model of an autoregressive stochastic generative model for images. This model cont... [more] | IT2020-108 SIP2020-86 RCS2020-199 pp.253-258 |
| IT | 2020-12-02 09:20 |
Online | Online | Performance Limit of Classification in the Presence of Label Noise with Erasure Goki Yasuda, Tota Suko, Manabu Kobayashi, Toshiyasu Matsushima (Waseda Univ.) IT2020-29 |
[more] | IT2020-29 pp.26-31 |
| IT | 2020-12-02 09:40 |
Online | Online | Approximation Method for Bayes Optimal Prediction in Phoneme Recognition Problem Taishi Yamaoka, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-30 |
In this paper, we propose a method of phoneme recognition. In the previous studies on phoneme recognition using the Hidd... [more] | IT2020-30 pp.32-37 |
| IT | 2020-12-02 10:00 |
Online | Online | Policy Optimization Based on Bayesian Decision Theory in Learning Period on Markov Decision Process Naoki Ichijo, Yuta Nakahara, Yuto Motomura, Toshiyasu Matsushima (Waseda Univ.) IT2020-31 |
In Markov decision process(MDP) problems with an unknown transition probability, a learning agent has to learn the unkno... [more] | IT2020-31 pp.38-43 |
| IT | 2020-12-02 10:30 |
Online | Online | Error Probability of Classification Based on the Analysis of the Bayes Code
-- Extension and Example -- Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-32 |
Suppose that we have two training sequences generated by parametrized distributions $P_{theta^*}$ and $P_{xi^*}$, where ... [more] | IT2020-32 pp.44-49 |
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