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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCC, ISEC, IT, WBS | 2024-03-14 10:20 |
Osaka | Osaka Univ. (Suita Campus) |  IT2023-111 ISEC2023-110 WBS2023-99 RCC2023-93 |
This paper deals with variable-length lossy source coding in which the criteria are a cumulant generating function of co... [more] | IT2023-111 ISEC2023-110 WBS2023-99 RCC2023-93 pp.238-240 |
| IT | 2023-08-03 15:20 |
Kanagawa | Shonan Institute of Technology (Primary: On-site, Secondary: Online) |
Bayesian Predictive Distribution for Poisson Observables Under a Class of Prior Distribution and $alpha$-Divergence Shota Saito (Gunma Univ.) IT2023-19 |
We investigate a Bayesian predictive distribution for independent Poisson observables. The alpha-divergence is adopted a... [more] | IT2023-19 pp.29-32 |
| RCC, ISEC, IT, WBS | 2023-03-14 10:30 |
Yamaguchi | (Primary: On-site, Secondary: Online) |
[Invited Talk]
Information-Theoretic Analyses for Two Problems Taking Security into Consideration
-- Parameter Estimation Problem under Local Differential Privacy, and Privacy-Utility Tradeoff Problem -- Shota Saito (Gunma Univ.) IT2022-71 ISEC2022-50 WBS2022-68 RCC2022-68 |
This lecture surveys the following two problems: 1) parameter estimation problem under $(epsilon, delta)$-local differen... [more] | IT2022-71 ISEC2022-50 WBS2022-68 RCC2022-68 pp.19-24 |
| IT, RCS, SIP | 2023-01-24 10:50 |
Gunma | Maebashi Terrsa (Primary: On-site, Secondary: Online) |
Lower Bound on Maximum Redundancy of Predictive Source Coding for Context Tree Source Shota Saito (Gunma Univ.) IT2022-37 SIP2022-88 RCS2022-216 |
In [Krichevskiy, IEEE Trans. Inf. Theory, vol.44, no.1, pp.296--303, 1998], a lower bound of the maximum redundancy of a... [more] | IT2022-37 SIP2022-88 RCS2022-216 pp.48-50 |
| IT, EMM | 2022-05-18 12:40 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
On Bayesian Approach for Classification of Context Tree Model Shota Saito (Gunma Univ.) IT2022-11 EMM2022-11 |
This study deals with the Bayesian classification problem, which was investigated by Merhav and Ziv [IEEE Trans. Inf. Th... [more] | IT2022-11 EMM2022-11 pp.56-60 |
| 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 |
| RCS, SIP, IT | 2022-01-21 13:55 |
Online | Online | Meta-Bound for Lower Bounds of Bayes Risk Shota Saito (Gunma Univ.) IT2021-82 SIP2021-90 RCS2021-250 |
In the parameter estimation problem of statistics and machine learning, information-theoretic lower bounds of the Bayes ... [more] | IT2021-82 SIP2021-90 RCS2021-250 pp.301-305 |
| IT | 2021-07-09 13:00 |
Online | Online | Bayesian Optimal Prediction and Its Approximation Algorithm for the Difference of Response Variables with and without Measures Considering Individual Differences by Assuming Latent Clusters Taisuke Ishiwatari (Waseda Univ.), Shota Saito (Gunma Univ.), Toshiyasu Matsushima (Waseda Univ.) IT2021-23 |
In observational studies, there are problems such as "the measure can be given only once to the target" and "the charact... [more] | IT2021-23 pp.45-50 |
| 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 |
| 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 10:30 |
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 |
| IT | 2020-07-16 14:45 |
Online | Online | Asymptotic Evaluation of $alpha$-divergence between VB Posterior Predictive Distribution and Bayesian Predictive Distribution Kazuki Yamada, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-14 |
In this paper, we consider the problem of determining probability distribution of $X_{n+1}$ given ${X_i }_{i=1}^{n}$ fol... [more] | IT2020-14 pp.19-23 |
| IT | 2019-07-25 14:25 |
Tokyo | NATULUCK-Iidabashi-Higashiguchi Ekimaeten | Bayes Optimal Prediction and Its Approximative Algorithm on Model Including Cluster Explanatory Variables and Regression Explanatory Variables Haruka Murayama, Shota Saito, Yuta Nakahara, Toshiyasu Matsushima (Waseda Univ.) IT2019-16 |
In this research, data are assumed to be divided in clusters based on a part of the continuous explanatory variables, an... [more] | IT2019-16 pp.5-10 |
| IT | 2019-07-25 14:50 |
Tokyo | NATULUCK-Iidabashi-Higashiguchi Ekimaeten | Bayes Optimal Classification on Decision Tree Model and Its Approximative Algorithm Using Ensemble Learning Nao Dobashi, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2019-17 |
In this paper we consider classification problem about discrete category $y$ regarding discrete variables $bm{x}$. Deci... [more] | IT2019-17 pp.11-16 |
| WBS, IT, ISEC | 2018-03-08 09:00 |
Tokyo | Katsusika Campas, Tokyo University of Science | Evaluation of Bayes Predictive Distributions under Unknown Parametric Models on Misspecified Models Hirokazu Kono, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2017-103 ISEC2017-91 WBS2017-84 |
In this paper, we discuss a problem to estimate the conditional distribution of $Y_{N+1}$ given $x_{N+1}$ and ${(x_t,y_t... [more] | IT2017-103 ISEC2017-91 WBS2017-84 pp.1-6 |
| IBISML | 2018-03-05 17:25 |
Fukuoka | Nishijin Plaza, Kyushu University | Bayesian Independent Component Analysis under Hierarchical Model on Latent Variables Kai Asaba, Shota Saito, Shunsuke Horii, Toshiyasu Matsushima (Waseda Univ.) IBISML2017-97 |
Independent component analysis (ICA) deals with the problem of estimating unknown latent variables which generate the ob... [more] | IBISML2017-97 pp.49-53 |
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