b'Jun Sakuma'

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Featured Co-authors

Ichiro Takeuchi
68 publications
Youhei Akimoto
34 publications
Kazuto Fukuchi
20 publications
Chia-Mu Yu
19 publications
Hideya Ochiai
16 publications
Yuwei Sun
13 publications
Hiromu Yakura
13 publications
Kaiwen Xu
10 publications
Ryo Furukawa
9 publications
Hiroyuki Hanada
8 publications
Tatsuya Mori
6 publications

research

∙ 05/27/2023

Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs

A concept-based classifier can explain the decision process of a deep le...

0 Kaiwen Xu, et al. ∙

research

∙ 04/17/2023

Heterogeneous Domain Adaptation with Positive and Unlabeled Data

Heterogeneous unsupervised domain adaptation (HUDA) is the most challeng...

0 Junki Mori, et al. ∙

research

∙ 04/02/2023

Instance-level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space

Malicious perturbations embedded in input data, known as Trojan attacks,...

0 Yuwei Sun, et al. ∙

research

∙ 03/28/2023

Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min–Max Optimization and its Application to Berthing Control Tasks

In this study, we consider a continuous min–max optimization problem min...

0 Atsuhiro Miyagi, et al. ∙

research

∙ 01/31/2023

Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning

We investigate policy transfer using image-to-semantics translation to m...

0 Rei Sato, et al. ∙

research

∙ 11/29/2022

Adaptive Scenario Subset Selection for Worst-Case Optimization and its Application to Well Placement Optimization

In this study, we consider simulation-based worst-case optimization prob...

0 Atsuhiro Miyagi, et al. ∙

research

∙ 11/07/2022

Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification

In the field of reinforcement learning, because of the high cost and ris...

0 Takumi Tanabe, et al. ∙

research

∙ 09/26/2022

Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient and their monotonic transformations

Evolution strategy (ES) is one of promising classes of algorithms for bl...

0 Daiki Morinaga, et al. ∙

research

∙ 09/22/2022

CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy

In real-world applications of multi-class classification models, misclas...

22 Daiki Nishiyama, et al. ∙

research

∙ 06/23/2022

Minimax Optimal Fair Regression under Linear Model

We investigate the minimax optimal error of a fair regression problem un...

0 Kazuto Fukuchi, et al. ∙

research

∙ 04/06/2022

Black-Box Min–Max Continuous Optimization Using CMA-ES with Worst-case Ranking Approximation

In this study, we investigate the problem of min-max continuous optimiza...

0 Atsuhiro Miyagi, et al. ∙

research

∙ 03/22/2022

Semi-Targeted Model Poisoning Attack on Federated Learning via Backward Error Analysis

Model poisoning attacks on federated learning (FL) intrude in the entire...

0 Yuwei Sun, et al. ∙

research

∙ 09/09/2021

Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation

We aim to explain a black-box classifier with the form: `data X is class...

0 Thien Q. Tran, et al. ∙

research

∙ 08/20/2021

Application of Adversarial Examples to Physical ECG Signals

This work aims to assess the reality and feasibility of the adversarial ...

0 Taiga Ono, et al. ∙

research

∙ 04/13/2021

Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution

Video game level generation based on machine learning (ML), in particula...

0 Takumi Tanabe, et al. ∙

research

∙ 03/02/2021

Convergence Rate of the (1+1)-Evolution Strategy with Success-Based Step-Size Adaptation on Convex Quadratic Functions

The (1+1)-evolution strategy (ES) with success-based step-size adaptatio...

0 Daiki Morinaga, et al. ∙

research

∙ 12/11/2020

AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment

Neural architecture search (NAS) is an approach for automatically design...

0 Rei Sato, et al. ∙

research

∙ 08/24/2020

Statistically Significant Pattern Mining with Ordinal Utility

Statistically significant patterns mining (SSPM) is an essential and cha...

0 Thien Q. Tran, et al. ∙

research

∙ 08/22/2020

Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs

Search engine logs have a great potential in tracking and predicting out...

0 Thien Q. Tran, et al. ∙

research

∙ 11/20/2019

Generate (non-software) Bugs to Fool Classifiers

In adversarial attacks intended to confound deep learning models, most s...

17 Hiromu Yakura, et al. ∙

research

∙ 05/27/2019

Locally Differentially Private Minimum Finding

We investigate a problem of finding the minimum, in which each user has ...

0 Kazuto Fukuchi, et al. ∙

research

∙ 01/18/2019

Robust Watermarking of Neural Network with Exponential Weighting

Deep learning has been achieving top performance in many tasks. Since tr...

0 Ryota Namba, et al. ∙

research

∙ 11/28/2018

Minimax Optimal Additive Functional Estimation with Discrete Distribution

This paper addresses a problem of estimating an additive functional give...

0 Kazuto Fukuchi, et al. ∙

research

∙ 11/28/2018

General-to-Detailed GAN for Infrequent Class Medical Images

Deep learning has significant potential for medical imaging. However, si...

0 Tatsuki Koga, et al. ∙

research

∙ 10/28/2018

Robust Audio Adversarial Example for a Physical Attack

The success of deep learning in recent years has raised concerns about a...

0 Hiromu Yakura, et al. ∙

research

∙ 03/01/2018

Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values

The problem of machine learning with missing values is common in many ar...

0 Hiroyuki Hanada, et al. ∙

research

∙ 01/12/2018

Minimax Optimal Additive Functional Estimation with Discrete Distribution: Slow Divergence Speed Case

This paper addresses an estimation problem of an additive functional of ...

0 Kazuto Fukuchi, et al. ∙

research

∙ 10/20/2017

Differentially Private Empirical Risk Minimization with Input Perturbation

We propose a novel framework for the differentially private ERM, input p...

0 Kazuto Fukuchi, et al. ∙

research

∙ 06/06/2017

Recommendation with k-anonymized Ratings

Recommender systems are widely used to predict personalized preferences ...

0 Jun Sakuma, et al. ∙

research

∙ 06/01/2016

Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization

We study large-scale classification problems in changing environments wh...

0 Hiroyuki Hanada, et al. ∙

research

∙ 02/15/2016

Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization

Privacy concern has been increasingly important in many machine learning...

0 Toshiyuki Takada, et al. ∙

research

∙ 11/06/2015

Neutralized Empirical Risk Minimization with Generalization Neutrality Bound

Currently, machine learning plays an important role in the lives and ind...

0 Kazuto Fukuchi, et al. ∙

research

∙ 06/25/2015

Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences

Fairness-aware learning is a novel framework for classification tasks. L...

0 Kazuto Fukuchi, et al. ∙

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