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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IBISML | 2024-12-20 10:50 |
Hokkaido | Lecture room 1 (D101), Graduate School of Environmental Science (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Active learning considering test distribution with Gaussian process regression model Yoshito Okura (Nagoya Univ.), Shion Takeno (Nagoya Univ./RIKEN), Yu Inatsu (NITech), Tatsuya Aoyama, Tomonari Tanaka, Satoshi Akahane (Nagoya Univ.), Hiroyuki Hanada, Noriaki Hashimoto (RIKEN), Taro Murayama, Lee Hanju, Shinya Kojima (DENSO), Ichiro Takeuchi (Nagoya Univ./RIKEN)  IBISML2024-37 |
Active learning is a method to construct a model with high prediction accuracy with a small number of data by adaptively... [more] | IBISML2024-37 pp.41-48 |
| IBISML | 2024-12-21 15:30 |
Hokkaido | Lecture room 1 (D101), Graduate School of Environmental Science (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Distributionally Robust Training Instances Selection with Guaranteed Model Performance Tomonari Tanaka (Nagoya Univ.), Hiroyuki Hanada (RIKEN), Hanting Yang (Nagoya University), Tatsuya Aoyama (Nagoya Univ.), Yu Inatsu (NITech), Satoshi Akahane, Yoshito Okura (Nagoya Univ.), Noriaki Hashimoto (RIKEN), Taro Murayama, Lee Hanju, Shinya Kojima (DENSO), Ichiro Takeuchi (Nagoya Univ./RIKEN) IBISML2024-53 |
In a problem setting where the data distribution differs between the development and operational phases, there exists a ... [more] | IBISML2024-53 pp.151-158 |
| IBISML, NC, IPSJ-BIO, IPSJ-MPS [detail] | 2024-06-20 15:50 |
Okinawa | OIST (Okinawa) | Distributionally Robust Safe Sample Screening and Its Application to Infinite-width Deep Neural Networks Tatsuya Aoyama (Nagoya Univ.), Hiroyuki Hanada (RIKEN), Satoshi Akahane, Yoshito Okura, Tomonari Tanaka (Nagoya Univ.), Yu Inatsu (NITech), Noriaki Hashimoto (RIKEN), Taro Murayama, Lee Hanju, Shinya Kojima (DENSO), Ichiro Takeuchi (Nagoya Univ.) NC2024-10 IBISML2024-10 |
In machine learning, handling large datasets has been problematic in computational resources. For this issue, safe sampl... [more] | NC2024-10 IBISML2024-10 pp.67-72 |
| MI | 2024-03-03 09:17 |
Okinawa | OKINAWAKEN SEINENKAIKAN (Okinawa, Online) (Primary: On-site, Secondary: Online) |
[Short Paper]
Valid p-value for critical instances in multiple instance learning Noriaki Hashimoto (RIKEN), Daiki Miwa (Nitech), Kosei Sumida (Nagoya Univ.), Hiroyuki Hanada (RIKEN), Hiroaki Miyoshi (Kurume Univ.), Jun Sakuma (Tokyo Tech/RIKEN), Hidekata Hontani (Nitech), Koichi Ohshima (Kurume Univ.), Ichiro Takeuchi (Nagoya Univ./RIKEN) MI2023-31 |
(To be available after the conference date) [more] | MI2023-31 pp.3-6 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-03 16:25 |
Hokkaido | Future University Hakodate (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Fast Identification of Possible Model Parameter Update for Low-Rank Update of Training Data Hiroyuki Hanada, Noriaki Hashimoto (RIKEN), Kouichi Taji, Ichiro Takeuchi (Nagoya Univ.) PRMU2022-123 IBISML2022-130 |
Machine learning methods often require re-training the training dataset with low-rank modifications (small number of ins... [more] | PRMU2022-123 IBISML2022-130 pp.347-354 |
| IBISML | 2022-09-15 15:05 |
Kanagawa | Keio Univ. (Yagami Campus) (Kanagawa, Online) (Primary: On-site, Secondary: Online) |
Improving Efficiency of Regularization Path Computation in Safe Pattern Pruning via Multiple Referential Solutions Takumi Yoshida (Nitech), Hiroyuki Hanada (RIKEN), Kazuya Nakagawa, Shinya Suzumura, Onur Boyar, Kazuki Iwata (Nitech), Shun Shimura, Yuji Tanaka (NaogyaU), Masayuki Karasuyama (Nitech), Kouichi Taji (NaogyaU), Koji Tsuda (UTokyo/RIKEN), Ichiro Takeuchi (NaogyaU/RIKEN) IBISML2022-38 |
Safe Screening and Safe Pattern Pruning are methods for efficiently modeling high-dimensional features by $L_1$-regulari... [more] | IBISML2022-38 pp.39-46 |
| MI | 2022-07-08 14:00 |
Hokkaido | (Hokkaido, Online) (Primary: On-site, Secondary: Online) |
Cell type-specific tumor degree estimation in malignant lymphoma pathology images Hiroki Masuda (NITech), Noriaki Hashimoto (RIKEN), Yusuke Takagi (NITech), Hiroyuki Hanada (RIKEN), Hiroaki Miyoshi, Kensaku Sato, Koichi Oshima (Kurume Univ.), Hidekata Hontani (NITech), Ichiro Takeuchi (Nagoya Univ./RIKEN) MI2022-32 |
In the pathological diagnosis flow of malignant lymphoma, a type of blood cancer, it is important to identify the type o... [more] | MI2022-32 pp.1-6 |
| IBISML | 2018-03-06 10:00 |
Fukuoka | Nishijin Plaza, Kyushu University (Fukuoka) | Learning rule-base model by Safe Pattern Pruning Hiroki Kato, Hiroyuki Hanada (Nagoya Inst. of Tech.), Ichiro Takeuchi (Nagoya Inst. of Tech./RIKEN/NIMS) IBISML2017-98 |
We consider learning the prediction model called ''rule-base model''. Rule-base model is the model which uses ''rules'' ... [more] | IBISML2017-98 pp.55-62 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. (Kyoto) | Empirical risk minimization for interval data and its applications to privacy preservations Hiroyuki Hanada, Toshiyuki Takada, Atsushi Shibagaki (NITech), Jun Sakuma (Univ. of Tsukuba), Ichiro Takeuchi (NITech) IBISML2016-89 |
In this research, for machine learning tasks, we consider that the values in the training data are given as intervals an... [more] | IBISML2016-89 pp.305-312 |
| PRMU, IPSJ-CVIM, IBISML [detail] | 2016-09-06 10:45 |
Toyama | (Toyama) | A proposal on quick sensitivity analysis of empirical risk minimization problems Hiroyuki Hanada, Atsushi Shibagaki (NITech), Jun Sakuma (Univ. of Tsukuba), Ichiro Takeuchi (NITech) PRMU2016-80 IBISML2016-35 |
For a training data set consisting of $n$ vectors of $d$ dimensions, we consider obtaining a training result from it by ... [more] | PRMU2016-80 IBISML2016-35 pp.203-210 |
| IBISML | 2015-11-27 14:00 |
Ibaraki | Epochal Tsukuba (Ibaraki) | [Poster Presentation]
Secure Approximation Guarantee for Private Empirical Risk Minimization with Homomorphic Encryption Toshiyuki Takada, Hiroyuki Hanada (NIT), Jun Sakuma (Univ.Tsukuba), Ichiro takeuchi (NIT) IBISML2015-86 |
Privacy concern has been increasingly important in many machine learning problems. In this paper, we study empirical ris... [more] | IBISML2015-86 pp.249-256 |
| PRMU, DE | 2008-06-19 14:00 |
Hokkaido | Otaru-Shimin-Kaikan (Hokkaido) | A study on fast search of the nearest string in edit distance Hiroyuki Hanada, Mineichi Kudo (Hokkaido Univ.) DE2008-8 PRMU2008-26 |
The problem is finding the nearest string, measured by edit distance, to a query string $q$ from a set $T$ of strings. ... [more] |
DE2008-8 PRMU2008-26 pp.41-46 |
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