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Committee Date Time Place Paper Title / Authors Abstract Paper #
IBISML 2023-12-21
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)
IBISML 2022-12-23
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
IBISML 2022-12-23
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
IA, ICSS 2022-06-24
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
IBISML 2022-01-17
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
IBISML 2022-01-18
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
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
IBISML 2018-11-05
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]
IBISML 2018-11-05
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
HWS, ISEC, SITE, ICSS, EMM, IPSJ-CSEC, IPSJ-SPT [detail] 2018-07-25
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
IBISML 2017-11-09
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
IBISML 2017-11-09
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
IBISML 2017-11-09
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
IBISML 2017-11-10
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
IBISML 2016-11-16
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]
IBISML 2016-11-16
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]
IBISML 2016-11-17
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
IBISML 2016-11-17
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
PRMU, IPSJ-CVIM, IBISML [detail] 2016-09-06
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
IBISML 2015-11-26
Ibaraki Epochal Tsukuba [Poster Presentation] Theoretical Vulnerability Evaluation on Linear Classifier under Self-Information Controllable Settings
Shohei Kobayashi (Univ. of Tsukuba), Shota Okumura, Ichiro Takeuchi (nitech), Jun Sakuma (Univ. of Tsukuba) IBISML2015-64
Self-information controllable learning is machine learning algorithms that allow data providers to control their data ev... [more] IBISML2015-64
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