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All Technical Committee Conferences (Searched in: Recent 10 Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2024-12-21 11:10 |
Hokkaido | Lecture room 1 (D101), Graduate School of Environmental Science (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Selective Inference for Auto Feature Engineering Tatsuya Matsukawa, Tomohiro Shiraishi (Nagoya Univ.), Shuichi Nishino (Nagoya Univ./RIKEN), Teruyuki Katsuoka (Nagoya Univ.), Ichiro Takeuchi (Nagoya Univ./RIKEN)  IBISML2024-46 |
Auto Feature Engineering (AFE) is the process of automatically generating meaningful features from raw data to improve t... [more] | IBISML2024-46 pp.100-107 |
| IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] | 2024-06-20 14:20 |
Okinawa | OIST (Okinawa) | Anomaly Detection in the Frequency Domain with Statistical Reliability Akifumi Yamada, Kouichi Taji (Nagoya Univ.), Ichiro Takeuchi (Nagoya Univ./RIKEN) NC2024-7 IBISML2024-7 |
There are many applications of artificial intelligence (AI) in the field of anomaly detection in the frequency domain fo... [more] | NC2024-7 IBISML2024-7 pp.43-50 |
| IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] | 2024-06-20 15:00 |
Okinawa | OIST (Okinawa) | Selective Inference for Reliability Quantification of k-Nearest Neighbor Anomoaly Detection Mizuki Niihori (Nagoya Univ.), Masaya Ikuta (NITech), Akihumi Yamada, Kouichi Taji, Ichiro Takeuchi (Nagoya Univ.) NC2024-8 IBISML2024-8 |
Currently, the $k$-nearest neighbor method is widely used in the field of machine learning anomaly detection. This met... [more] |
NC2024-8 IBISML2024-8 pp.51-59 |
| IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] | 2024-06-20 15:25 |
Okinawa | OIST (Okinawa) | Selective Inference for Anomaly Detection using Diffusion Models Teruyuki Katsuoka, Tomohiro Shiraishi (Nagoya Univ.), Daiki Miwa (NITech), Vo Nguyen Le Duy (RIKEN), Ichiro Takeuchi (Nagoya Univ./RIKEN) NC2024-9 IBISML2024-9 |
In recent years, there has been active research on anomaly detection using diffusion models, which are generative models... [more] | NC2024-9 IBISML2024-9 pp.60-66 |
| NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] | 2023-06-29 15:10 |
Okinawa | OIST Conference Center (Okinawa, Online) (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 (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 (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 (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) (Hokkaido) | [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) (Hokkaido) | [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) (Hokkaido) | [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 (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 (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. (Kyoto) | 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 | (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 (Okinawa) | 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 |
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