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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| MI | 2024-03-03 09:17 |
Okinawa | OKINAWAKEN SEINENKAIKAN (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 | 2024-03-04 10:40 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Poisoning Attack on Fairness of Fair Classification Algorithm through Threshold Control Dai Shengtian, Akimoto Youhei (Univ. of Tsukuba/RIKEN), Jun Sakuma (Tokyo Tech./RIKEN), Fukuchi Kazuto (Univ. of Tsukuba/RIKEN) IBISML2023-47 |
The ethical issues of artificial intelligence have become more severe as machine learning is widely used in several fiel... [more] | IBISML2023-47 pp.49-56 |
| IBISML | 2023-12-21 11:20 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
Classification Error Analysis under Covariate Shift between Non-absolutely Continuous Distributions through neighbor-transfer-exponent Mitsuhiro Fujikawa, Youhei Akimoto (Univ. of Tsukuba), Jun Sakuma (Tokyo Inst. of Tech.), Kazuto Fukuchi (Univ. of Tsukuba) IBISML2023-39 |
Transfer learning is considered successful when increasing the source sample size decreases the target sample size neede... [more] | IBISML2023-39 pp.58-65 |
| IBISML | 2022-12-23 10:30 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Statistically Significant Concept-based Explanation via Model Knockoffs Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma (Univ. Tsukuba/RIKEN AIP) IBISML2022-54 |
[more] | IBISML2022-54 pp.78-85 |
| IBISML | 2022-12-23 10:50 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
Interpretable Deep Image Classifier with Class-distinguishable Concept Text Kazuhiro Saito, Kazuto Fukuchi (Univ.Tsukuba), Jun Sakuma (Univ.Tsukuba/RIKEN) IBISML2022-55 |
(To be available after the conference date) [more] | IBISML2022-55 pp.86-93 |
| IA, ICSS | 2022-06-24 10:25 |
Nagasaki | Univ. of Nagasaki (Primary: On-site, Secondary: Online) |
Application of Adversarial Examples to Physical ECG Signals Taiga Ono (Waseda Univ.), Takeshi Sugawara (UEC), Jun Sakuma (Tsukuba Univ./RIKEN), Tatsuya Mori (Waseda Univ./RIKEN/NICT) IA2022-11 ICSS2022-11 |
This work aims to assess the reality and feasibility of applying adversarial examples to attack cardiac diagnosis system... [more] | IA2022-11 ICSS2022-11 pp.61-66 |
| IBISML | 2022-01-17 11:20 |
Online | Online | CAMRI Loss: Class-wise Additive Angular Margin Loss for Improving Recall of a Specific Class Daiki Nishiyama (Univ. Tsukuba), Fukuchi Kazuto, Yohei Akimoto, Jun Sakuma (Univ. Tsukuba/RIKEN) IBISML2021-22 |
In real-world applications of multiclass classification models, there is a need to increase the recall of classes where ... [more] | IBISML2021-22 pp.29-36 |
| IBISML | 2022-01-18 11:15 |
Online | Online | [Invited Talk]
TBA Jun Sakuma (Tsukuba Univ./RIKEN) |
Explainability is one of the key elements required in medical image diagnosis using deep image recognition models. In th... [more] | |
| IBISML | 2022-01-18 13:20 |
Online | Online | IBISML2021-24 |
We aim to explain a black-box classifier with the form: `data X is classified as class Y because X has A, B and does not... [more] | IBISML2021-24 pp.45-53 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Watermarking of Neural Network with Exponential Weighting Parameters Ryota Namba (Tsukuba Univ.), Jun Sakuma (Tsukuba Univ./riken) IBISML2018-63 |
Deep learning has been achieving top performance in many tasks. Since training of a deep learning model requires a gr... [more] |
IBISML2018-63 pp.143-150 |
| IBISML | 2018-11-05 15:10 |
Hokkaido | Hokkaido Citizens Activites Center (Kaderu 2.7) | [Poster Presentation]
Differential Privacy for Likelihood Ratio Test Arashi Haishima (Univ. of Tsukuba), Jun Sakuma (Univ. of Tsukuba/RIKEN) IBISML2018-86 |
Likelihood ratio test of logistic regression commonly used when testing whether an attribute of data significantly influ... [more] | IBISML2018-86 pp.313-320 |
| HWS, ISEC, SITE, ICSS, EMM, IPSJ-CSEC, IPSJ-SPT [detail] | 2018-07-25 16:40 |
Hokkaido | Sapporo Convention Center | Chi-Squared Test for Independence Between Single Nucleotide Polymorphisms and Diseases Using Mis-operation Resistant Searchable Homomorphic Encryption Keita Emura, Takuya Hayashi (NICT/JST), Wenjie Lu (University of Tsukuba/JST CREST), Shiho Moriai (NICT/JST), Jun Sakuma (University of Tsukuba/JST CREST/RIKEN), Yoshiji Yamada (Mie University/JST CREST) ISEC2018-31 SITE2018-23 HWS2018-28 ICSS2018-34 EMM2018-30 |
[more] | ISEC2018-31 SITE2018-23 HWS2018-28 ICSS2018-34 EMM2018-30 pp.171-176 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Online Optimization Method for Generalized $ell_1$ Regularized Problems Yoshihiro Nakazato, Kazuto Fukuchi (Tsukuba Univ.), Jun Sakuma (Tsukuba Univ./Riken/JST) IBISML2017-47 |
Structured sparse regularization is vital to enhance the precision and the interpretability of the model by introducing ... [more] | IBISML2017-47 pp.93-100 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Consequently Fair Contextual Bandit Learning Kazuto Fukuchi (Univ. of Tsukuba), Jun Sakuma (Univ. of Tsukuba/JST/RIKEN) IBISML2017-53 |
Fairness in machine learning is being recognized as an important field. It requires that the consequent decisions made b... [more] | IBISML2017-53 pp.139-146 |
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Fast Computation of Lower Bounds for Privacy Evaluations, Based on Binary Decision Diagrams Shogo Takeuchi (Univ. of Tokyo), Kosuke Kusano, Jun Sakuma (Univ. of Tsukuba), Koji Tsuda (Univ. of Tokyo) IBISML2017-60 |
An input value estimation is a privacy issue in a service provides information by personal information. It is necessary ... [more] | IBISML2017-60 pp.193-200 |
| IBISML | 2017-11-10 13:00 |
Tokyo | Univ. of Tokyo | Estimation of Training Data Distribution from Probabilistic Classifier using Generative Adversarial Networks Kosuke Kusano, Jun Sakuma (Univ. Tsukuba) IBISML2017-76 |
Suppose we have a deep classification model $f$ that is trained with private samples that should not be released, but we... [more] | IBISML2017-76 pp.301-308 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Proximal Average Accelerated Proximal Gradient Algorithm with Adaptive Restart Yoshihiro Nakazato, Kazuto Fukuchi, Jun Sakuma (Univ. Tsukuba) IBISML2016-55 |
When using multiple regularizers, their proximal mapping is not easily available in closed form. The method to calculat... [more] |
IBISML2016-55 pp.65-71 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Additive Model Decomposition with Global Sparse Structure for Multi-task Granger Causal Estimation Hitoshi Abe, Jun Sakuma (Univ. Tsukuba) IBISML2016-56 |
Causality estimation is one of the key issues in time-series data analysis. Granger causality is widely known as a form... [more] |
IBISML2016-56 pp.73-79 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | Minimax optimal estimator for additively decomposable scalar functionals of discrete distributions Kazuto Fukuchi, Jun Sakuma (Univ. of Tsukuba) IBISML2016-82 |
We deal with a problem of estimating additively decomposable scalar functionals from a set of $n$ iid samples drawn from... [more] | IBISML2016-82 pp.259-265 |
| IBISML | 2016-11-17 14:00 |
Kyoto | Kyoto Univ. | 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 |
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