Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
SIP, IT, RCS |
2024-01-18 09:30 |
Miyagi |
(Primary: On-site, Secondary: Online) |
The quantum decoding attaining the minimum guesswork with single-qubit side information Michele Dall'Arno (TUT) IT2023-36 SIP2023-69 RCS2023-211 |
The minimum guesswork quantifies the minimum number of queries needed to guess the value of a random variable, when only... [more] |
IT2023-36 SIP2023-69 RCS2023-211 pp.37-38 |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2023-06-29 13:30 |
Okinawa |
OIST Conference Center (Primary: On-site, Secondary: Online) |
Selective Inference for a Combination of Feature Selection Algorithms Tatsuya Matsukawa (Nagoya Univ.), Daiki Miwa (NITech), Vo Nguyen Le Duy (RIKEN), Koichi Taji (Nagoya Univ.), Ichiro Takeuchi (Nagoya Univ./RIKEN) NC2023-1 IBISML2023-1 |
In data-driven science, classical statistical hypothesis testing does not provide an adequate reliability assessment bec... [more] |
NC2023-1 IBISML2023-1 pp.1-8 |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2023-06-29 15:10 |
Okinawa |
OIST Conference Center (Primary: On-site, Secondary: Online) |
Selective Inference for DNN-driven Saliency Map Daiki Miwa (NITech), Vo Nguyen Le Duy (RIKEN), Tomohiro Shiraishi (Nagoya Univ.), Ichiro Takeuchi (Nagoya Univ./RIKEN) NC2023-5 IBISML2023-5 |
The usefulness of image classification using DNN models has been confirmed in various fields, but the prediction mechani... [more] |
NC2023-5 IBISML2023-5 pp.30-34 |
QIT (2nd) |
2023-05-29 16:30 |
Kyoto |
Katsura Campus, Kyoto University |
[Poster Presentation]
A parallel, branch and bound algorithm for a combinatorial instance of quantum hypothesis testing Baasanchimed Avirmed, Kaito Niinomi, Michele Dall'Arno (Toyohashi U. of Technology) |
[学生発表賞希望, application for student presentation award] We consider a particular instance of the quantum hypothesis testin... [more] |
|
IBISML |
2022-12-23 14:30 |
Kyoto |
Kyoto University (Primary: On-site, Secondary: Online) |
Selective Inference for Cluster Level Inference in Brain Image Analysis Masaya Ikuta, Mizuki Sato (NITech), Akifumi Yamada (Nagoya Univ.), Vo Nguyen Le Duy (NITech/RIKEN), Ryo Emoto (Nagoya Univ.), Yuko Ishimaru, Yuka Takao, Atsushi Kawaguchi (Saga Univ.), Shigeyuki Matsui (Nagoya Univ.), Ichiro Takeuchi (Nagoya Univ./RIKEN) IBISML2022-61 |
Cluster-level inference in brain image analysis is often employed to identify disease-related regions in brain disorders... [more] |
IBISML2022-61 pp.128-133 |
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 |
IT, ISEC, RCC, WBS |
2022-03-11 14:55 |
Online |
Online |
On Strong Converse Theorem for Distributed Hypothesis Testing Yasutada Oohama (UEC) IT2021-122 ISEC2021-87 WBS2021-90 RCC2021-97 |
In this study, we consider a communication system in which data
generated at two points with correlation is separatley... [more] |
IT2021-122 ISEC2021-87 WBS2021-90 RCC2021-97 pp.228-233 |
WBS, IT, ISEC |
2021-03-05 14:45 |
Online |
Online |
Analysis of Optimal Error Exponents on Classification for Markov Sources Hiroto Kuramata, Hideki Yagi, Tsutomu Kawabata (UEC) IT2020-152 ISEC2020-82 WBS2020-71 |
We consider a classification problem for a test sequence to determine from which source the sequence generates. The syst... [more] |
IT2020-152 ISEC2020-82 WBS2020-71 pp.245-250 |
SIP, IT, RCS |
2021-01-21 15:45 |
Online |
Online |
[Special Invited Talk]
Turbo Equalization to Lossless/Lossy Distributed Multiterminal Source Coding: How are they connected?
-- Towards Distributed Hypothesis Testing over IoT Networks -- Tadashi Matsumoto (JAIST) IT2020-80 SIP2020-58 RCS2020-171 |
A goal of this talk is to provide the audience with the knowledge about how the presenter's research experiences and foo... [more] |
IT2020-80 SIP2020-58 RCS2020-171 p.99 |
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 |
IBISML |
2020-10-22 09:05 |
Online |
Online |
IBISML2020-26 |
A new selective inference approach via multiscale bootstrap is proposed for general hypotheses conditioned on complicate... [more] |
IBISML2020-26 p.45 |
SR |
2019-12-06 13:00 |
Okinawa |
Ishigaki City Hall (Ishigaki Island) |
Radio Environment Map Updating Procedure based on Hypothesis Testing Keita Katagiri, Takeo Fujii (UEC) SR2019-104 |
(To be available after the conference date) [more] |
SR2019-104 pp.87-94 |
IBISML |
2018-11-05 15:10 |
Hokkaido |
Hokkaido Citizens Activites Center (Kaderu 2.7) |
[Poster Presentation]
Selective Inference for Feature Selection after Hierarchical Clustering Kenta Suzuki, Shigenori Inoue, Yuta Umezu (NIT), Ichiro Takeuchi (NIT/NIMS/RIKEN) IBISML2018-70 |
It is important to find characteristic features behind the data from, e.g., gene expression level or customer's purchase... [more] |
IBISML2018-70 pp.197-204 |
IBISML |
2018-11-05 15:10 |
Hokkaido |
Hokkaido Citizens Activites Center (Kaderu 2.7) |
[Poster Presentation]
Selective Inference for Dynamic Programming-based Sequence Segmentation Hiroki Toda, Yuta Umezu, Takuto Sakuma (NIT), Ichiro Takeuchi (NIT/NIMS/RIKEN) IBISML2018-81 |
Recently, a large number of sensor devices have enabled us to collect various kind of sequence data easily. Sequence seg... [more] |
IBISML2018-81 pp.279-286 |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2018-06-13 10:50 |
Okinawa |
Okinawa Institute of Science and Technology |
Post Clustering Inference, with Application to Single Cell Analysis Shigenori Inoue, Yuta Umezu (NIT), Shouma Tsubota (Nagoya Univ.), Ichiro Takeuchi (NIT/RIKEN/NIMS) IBISML2018-3 |
There are many data with several subgroups, such as customer data and gene expression data and so on. One way to analyze... [more] |
IBISML2018-3 pp.15-22 |
IBISML |
2017-11-09 13:00 |
Tokyo |
Univ. of Tokyo |
[Poster Presentation]
Post Clustering Inference for Heterogeneous Data Shigenori Inoue, Yuta Umezu (NIT), Shoma Tsubota (Nagoya Univ.), Ichiro Takeuchi (NIT/RIKEN/NIMS) IBISML2017-44 |
Along with the prevalence of Precision Medicine, the demand for analytical methods on heterogeneous data is increasing. ... [more] |
IBISML2017-44 pp.69-76 |
IBISML |
2017-11-09 13:00 |
Tokyo |
Univ. of Tokyo |
[Poster Presentation]
Learning huge Bayesian networks using RAI algorithm based on Bayes factor Kazuki Natori, Masaki Uto, Maomi Ueno (UEC) IBISML2017-58 |
``Learning Bayesian networks'' has NP-hard problem. The state-of-the-arts method of learning Bayesian networks cannot le... [more] |
IBISML2017-58 pp.177-184 |
IBISML |
2017-11-10 13:00 |
Tokyo |
Univ. of Tokyo |
[Poster Presentation]
Selective Inference for Change Point Detection in Multidimensional Sequence Yuta Umezu (Nitech), Ichiro Takeuchi (Nitech/RIKEN/NIMS) IBISML2017-71 |
In various fields such as engineering, bioinformatics and econometrics, detecting structural changes from a given sequen... [more] |
IBISML2017-71 pp.269-276 |
IBISML |
2017-11-10 13:00 |
Tokyo |
Univ. of Tokyo |
[Poster Presentation]
Correcting selection bias in active learning based on selective inference framework Yu Inatsu (RIKEN), Ichiro Takeuchi (Nitech/RIKEN/NIMS) IBISML2017-74 |
Consider the active learning that constructs regression model from given data and actually observes the value at the po... [more] |
IBISML2017-74 pp.289-296 |
MBE, NC (Joint) |
2016-12-07 13:55 |
Aichi |
Toyohashi University of Technology |
A Study of the System to Control Static Upright Posture of Humans Jono Yusuke, Takada Hiroki (Fukui Univ) MBE2016-62 |
Humans spend most of their everyday life in standing position. If it were difficult to maintain an upright posture, we w... [more] |
MBE2016-62 pp.31-34 |