71 citations · 368 across the 49 of their papers we have counts for
53 papers
Beyond IGO-Flow: Toward Convergence Analysis of IGO in Continuous Spaces
Ryosuke Kimura, Youhei Akimoto
Information-Geometric Optimization (IGO) provides a unified framework for black-box optimization by interpreting the adaptation of a search distribution as a natural gradient updat…
Mixed-Categorical Black-Box Optimization via Information-Geometric Bilevel Decomposition
Marc Ong, Shinichi Shirakawa, Youhei Akimoto
Mixed categorical-continuous optimization arises in many practical domains, yet remains challenging. In the black-box setting, evolution strategy-based approaches have shown promis…
Accelerating Black-Box Bilevel Optimization with Rank-Based Upper-Level Value Function Approximation
Marc Ong, Youhei Akimoto
Bilevel optimization is a field of significant theoretical and practical interest, yet solving such optimization problems remains challenging. Evolutionary methods have been employ…
Feature selection based on cluster assumption in PU learning
Motonobu Uchikoshi, Youhei Akimoto
Feature selection is essential for efficient data mining and sometimes encounters the positive-unlabeled (PU) learning scenario, where only a few positive labels are available, whi…
Challenges of Interaction in Optimizing Mixed Categorical-Continuous Variables
Youhei Akimoto, Xilin Gao, Ze Kai Ng +1
Optimization of mixed categorical-continuous variables is prevalent in real-world applications of black-box optimization. Recently, CatCMA has been proposed as a method for optimiz…
Probabilistic Prediction of Ship Maneuvering Motion using Ensemble Learning with Feedforward Neural Networks
Kouki Wakita, Youhei Akimoto, Atsuo Maki
In the field of Maritime Autonomous Surface Ships (MASS), the accurate modeling of ship maneuvering motion for harbor maneuvers is a crucial technology. Non-parametric system ident…
Conceptual Design on the Field of View of Celestial Navigation Systems for Maritime Autonomous Surface Ships
Kouki Wakita, Fuyuki Hane, Takeshi Sekiguchi +4
In order to understand the appropriate field of view (FOV) size of celestial automatic navigation systems for surface ships, we investigate the variations of measurement accuracy o…
Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm
Ryoki Hamano, Kento Uchida, Shinichi Shirakawa +2
The majority of theoretical analyses of evolutionary algorithms in the discrete domain focus on binary optimization algorithms, even though black-box optimization on the categorica…
Simultaneous optimization of control gains and reference filter coefficients for trajectory tracking control
Amane Sakanashi, Rin Suyama, Atsuo Maki +1
Research on vessel automation and autonomy is currently being conducted by various countries and institutions. Safe and accurate ship control algorithms are crucial to realize auto…
Theoretical Analysis of Explicit Averaging and Novel Sign Averaging in Comparison-Based Search
Daiki Morinaga, Youhei Akimoto
In black-box optimization, noise in the objective function is inevitable. Noise disrupts the ranking of candidate solutions in comparison-based optimization, possibly deteriorating…
CMA-ES with Learning Rate Adaptation
Masahiro Nomura, Youhei Akimoto, Isao Ono
The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving continuous black-box optimization problems. A practically useful aspe…
Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs
Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto +1
A concept-based classifier can explain the decision process of a deep learning model by human-understandable concepts in image classification problems. However, sometimes concept-b…
Data Augmentation Methods of Dynamic Model Identification for Harbor Maneuvers using Feedforward Neural Network
Kouki Wakita, Yoshiki Miyauchi, Youhei Akimoto +1
A dynamic model for an automatic berthing and unberthing controller has to estimate harbor maneuvers, which include berthing, unberthing, approach maneuvers to berths, and entering…
CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems?
Masahiro Nomura, Youhei Akimoto, Isao Ono
The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving black-box continuous optimization problems. One practically useful as…
Covariance Matrix Adaptation Evolutionary Strategy with Worst-Case Ranking Approximation for Min--Max Optimization and its Application to Berthing Control Tasks
Atsuhiro Miyagi, Yoshiki Miyauchi, Atsuo Maki +3
In this study, we consider a continuous min--max optimization problem whose objective function is a black-box. We propose a…
Development of a Mathematical Model for Harbor-Maneuvers to Realize Modeling Automation
Yoshiki Miyauchi, Youhei Akimoto, Naoya Umeda +1
A simulation environment of harbor maneuvers is critical for developing automatic berthing. Dynamic models are widely used to estimate harbor maneuvers. However, human decision-mak…
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning
Rei Sato, Kazuto Fukuchi, Jun Sakuma +1
We investigate policy transfer using image-to-semantics translation to mitigate learning difficulties in vision-based robotics control agents. This problem assumes two environments…
Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning
Kouki Wakita, Youhei Akimoto, Dimas M. Rachman +3
Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are ofte…
Adaptive Scenario Subset Selection for Worst-Case Optimization and its Application to Well Placement Optimization
Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma +1
In this study, we consider simulation-based worst-case optimization problems with continuous design variables and a finite scenario set. To reduce the number of simulations require…
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
Takumi Tanabe, Rei Sato, Kazuto Fukuchi +2
In the field of reinforcement learning, because of the high cost and risk of policy training in the real world, policies are trained in a simulation environment and transferred to…
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy
Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto +1
In real-world applications of multi-class classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (…
Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient
Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma +1
Evolution strategy (ES) is one of the promising classes of algorithms for black-box continuous optimization. Despite its broad successes in applications, theoretical analysis on th…
Black-Box Min--Max Continuous Optimization Using CMA-ES with Worst-case Ranking Approximation
Atsuhiro Miyagi, Kazuto Fukuchi, Jun Sakuma +1
In this study, we investigate the problem of min-max continuous optimization in a black-box setting . A popular approach updates and simultaneously…
Monotone Improvement of Information-Geometric Optimization Algorithms with a Surrogate Function
Youhei Akimoto
A surrogate function is often employed to reduce the number of objective function evaluations for optimization. However, the effect of using a surrogate model in evolutionary appro…