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 Results 1 - 12 of 12  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
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
PRMU 2021-12-16
10:45
Online Online Selective Inference for Multi-dimensional Multiple Change-Points
Ryota Sugiyama, Hiroki Toda (NIT), Vo Nguyen Le Duy (NIT/RIKEN), Yu Inatsu (NIT), Ichiro Takeuchi (NIT/RIKEN) PRMU2021-28
Detecting changes in the average structure of multi-dimensional sequence is an important task in various fields. Since c... [more] PRMU2021-28
pp.25-30
IBISML 2021-03-02
10:25
Online Online Selective Inference for Convex Clustering Using Parametric Programming
Yumehiro Omori, Yu Inatsu (Nitech), Ichiro Takeuchi (Nitech/RIKEN) IBISML2020-35
Traditional statistical inference assumes that the hypothesis is predetermined and cannot be used as is for statistical ... [more] IBISML2020-35
pp.9-15
IBISML 2021-03-03
15:15
Online Online Selective Inference for Change-point Detection in Multi-dimensional Series Data
Ryota Sugiyama, Hiroki Toda, Vo Nguyen Le Duy, Yu Inatsu (NIT), Ichiro Takeuchi (NIT/RIKEN) IBISML2020-51
Detecting changes of the average structures in a multi-dimensional sequence is an important task in various fields. In t... [more] IBISML2020-51
pp.63-70
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
IBISML 2018-11-05
15:10
Hokkaido Hokkaido Citizens Activites Center (Kaderu 2.7) [Poster Presentation] Active Learning in Sparse Linear Regression Models via Selective Inference
Yuta Umezu (NIT), Ichiro Takeuchi (NIT/NIMS/RIKEN) IBISML2018-95
In order to efficiently estimate interested parameter, one can design sampling strategy by defining some criterion on th... [more] IBISML2018-95
pp.381-388
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
IBISML 2016-11-16
15:00
Kyoto Kyoto Univ. Selective Inference for High-Dimensional Binary Classification
Yuta Umezu, Kazuya Nakagawa (NIT), Koji Tsuda (Univ. of Tokyo), Ichiro Takeuchi (NIT) IBISML2016-59
In machine learning and other related area, the number of features is often reduced by some feature selection procedure ... [more] IBISML2016-59
pp.93-100
PRMU, IPSJ-CVIM, IBISML [detail] 2016-09-05
13:15
Toyama   [Short Paper] Selective Inference for Time-series Change-Point Analysis
Yuta Umezu, Kazuya Nakagawa, Shigenori Inoue (NIT), Koji Tsuda (Tokyo Univ.), Mahito Sugiyama, Takuya Maekawa (Osaka Univ.), Toru Tamaki (Hiroshima Univ.), Ken Yoda (Nagoya Univ.), Ichiro Takeuchi (NIT) PRMU2016-63 IBISML2016-18
In this paper, we propose a statistical method for time series data after detecting a change point. Because the change p... [more] PRMU2016-63 IBISML2016-18
pp.89-92
NC, IPSJ-BIO, IBISML, IPSJ-MPS
(Joint) [detail]
2015-06-23
14:40
Okinawa Okinawa Institute of Science and Technology Selective inference for high-order interaction model
Shinya Suzumura, Kazuya Nakagawa (NIT), Koji Tsuda (UT), Ichiro Takeuchi (NIT) IBISML2015-11
Finding statistically significant high-order interaction features in predictive modeling is important but challenging ta... [more] IBISML2015-11
pp.69-74
 Results 1 - 12 of 12  /   
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