Youhei Akimoto

AI Chat

Featured Co-authors

Jun Sakuma
33 publications
Tobias Glasmachers
33 publications
Yann Ollivier
30 publications
Kazuto Fukuchi
20 publications
Shota Saito
20 publications
Ryoji Tanabe
19 publications
Nikolaus Hansen
14 publications
Anne Auger
14 publications
Shinichi Shirakawa
14 publications
Masahiro Nomura
13 publications
Hiromu Yakura
13 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

∙ 04/07/2023

CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?

The covariance matrix adaptation evolution strategy (CMA-ES) is one of t...

0 Masahiro Nomura, 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

∙ 12/13/2022

Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning

Automation of berthing maneuvers in shipping is a pressing issue as the ...

0 Kouki Wakita, 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

∙ 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

∙ 04/06/2022

Monotone Improvement of Information-Geometric Optimization Algorithms with a Surrogate Function

A surrogate function is often employed to reduce the number of objective...

0 Youhei Akimoto, et al. ∙

research

∙ 03/29/2022

A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization

This paper proposes a two-phase framework with a Bézier simplex-based in...

0 Ryoji Tanabe, 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

∙ 05/25/2021

Saddle Point Optimization with Approximate Minimization Oracle and its Application to Robust Berthing Control

We propose an approach to saddle point optimization relying only on an o...

0 Youhei Akimoto, 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

∙ 03/29/2021

Saddle Point Optimization with Approximate Minimization Oracle

A major approach to saddle point optimization min_xmax_y f(x, y) is a gr...

0 Youhei Akimoto, 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

∙ 12/13/2020

Warm Starting CMA-ES for Hyperparameter Optimization

Hyperparameter optimization (HPO), formulated as black-box optimization ...

0 Masahiro Nomura, 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

∙ 09/18/2020

Global Linear Convergence of Evolution Strategies on More Than Smooth Strongly Convex Functions

Evolution strategies (ESs) are zero-order stochastic black-box optimizat...

0 Youhei Akimoto, 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

∙ 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

∙ 05/21/2019

Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search

High sensitivity of neural architecture search (NAS) methods against the...

0 Youhei Akimoto, et al. ∙

research

∙ 05/14/2019

Diagonal Acceleration for Covariance Matrix Adaptation Evolution Strategies

We introduce an acceleration for covariance matrix adaptation evolution ...

0 Youhei Akimoto, et al. ∙

research

∙ 11/02/2018

Ranking Based Linear Constraint Handling Method with Adaptive Penalty

A novel linear constraint handling technique for the covariance matrix a...

0 Naoki Sakamoto, et al. ∙

research

∙ 09/18/2018

Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network

Black box discrete optimization (BBDO) appears in wide range of engineer...

0 Kouhei Nishida, et al. ∙

research

∙ 05/31/2018

Sample Reuse via Importance Sampling in Information Geometric Optimization

In this paper we propose a technique to reduce the number of function ev...

0 Shinichi Shirakawa, et al. ∙

research

∙ 02/09/2018

Drift Theory in Continuous Search Spaces: Expected Hitting Time of the (1+1)-ES with 1/5 Success Rule

This paper explores the use of the standard approach for proving runtime...

0 Youhei Akimoto, et al. ∙

research

∙ 01/23/2018

Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling

Deep neural networks (DNNs) are powerful machine learning models and hav...

0 Shinichi Shirakawa, et al. ∙

research

∙ 06/07/2017

Fast Eigen Decomposition for Low-Rank Matrix Approximation

In this paper we present an efficient algorithm to compute the eigen dec...

0 Youhei Akimoto, et al. ∙

research

∙ 11/16/2012

Objective Improvement in Information-Geometric Optimization

Information-Geometric Optimization (IGO) is a unified framework of stoch...

0 Youhei Akimoto, et al. ∙

research

∙ 06/21/2012

Convergence of the Continuous Time Trajectories of Isotropic Evolution Strategies on Monotonic C^2-composite Functions

The Information-Geometric Optimization (IGO) has been introduced as a un...

0 Youhei Akimoto, et al. ∙

research

∙ 06/04/2012

Theoretical foundation for CMA-ES from information geometric perspective

This paper explores the theoretical basis of the covariance matrix adapt...

0 Youhei Akimoto, et al. ∙

research

∙ 04/18/2012

Analysis of a Natural Gradient Algorithm on Monotonic Convex-Quadratic-Composite Functions

In this paper we investigate the convergence properties of a variant of ...

0 Youhei Akimoto, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world