50 citations · 60 across the 21 of their papers we have counts for
21 papers
SIGMA: SHAP-Guided Implicit-Trajectory Generation for Metadata-Free LLM-Based AutoFE
Xuan Zheng, Kento Uchida, Shinichi Shirakawa
Recent research has leveraged Large Language Models (LLMs) to enhance Automated Feature Engineering (AutoFE) through semantic descriptions and trajectory-based prompting. However,…
BBOWP-Bench: Evaluating LLMs on Black-Box Optimization Word Problems
Yutaro Yamada, Kei Hiroshima, Nozomu Yoshinari +2
Formulating an optimization problem strongly affects the quality of the final solution, yet good formulations usually require substantial expertise. Recent studies have therefore e…
Neural Architecture Search of Sample Reweighting Networks for Complex Distribution Shift
Keisuke Sugawara, Kento Uchida, Shinichi Shirakawa
Sample reweighting is a major approach to addressing distribution shifts, such as label noise and class imbalance. Meta-Weight-Net (MW-Net) is a promising sample reweighting networ…
Weight Adaptation for Improving Parallel Performance of Adaptive Stochastic Natural Gradient
Yutaro Yamada, Kento Uchida, Shinichi Shirakawa
Probabilistic model-based evolutionary algorithms are promising for black-box optimization. Specifically, the adaptive stochastic natural gradient (ASNG) adaptively updates its lea…
OnDeFog: Online Decision Transformer under Frame Dropping
Daiki Yotsufuji, Kenta Nishihara, Shoma Shimizu +2
In challenging real-world reinforcement learning applications, communication delays or sensor failures often cause frame dropping, in which the agent cannot receive the dropped sta…
Convergence Analysis of Evolution Strategies for Mixed-Integer Optimization
Ryoki Hamano, Kento Uchida, Shinichi Shirakawa
Mixed-integer extensions of evolution strategies (ES) that discretize selected coordinates of sampled continuous vectors often impose a lower bound on the standard deviation of int…
Adaptive Stochastic Natural Gradient Method for Safe Optimization on Binary Space
Kento Uchida, Ryoki Hamano, Masahiro Nomura +1
Optimization problems in real-world applications across the medical and engineering domains often involve potential risks when evaluating candidate solutions. Safe optimization aim…
Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning
Kei Hiroshima, Kento Uchida, Shinichi Shirakawa
Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge. Recent advances in large pre-t…
Surrogate Benchmarks for Model Merging Optimization
Rio Akizuki, Yuya Kudo, Nozomu Yoshinari +4
Model merging techniques aim to integrate the abilities of multiple models into a single model. Most model merging techniques have hyperparameters, and their setting affects the pe…
Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers
Hikaru Sawafuji, Ryota Ozaki, Takuto Motomura +4
Self-localization is an important technology for automating bulldozers. Conventional bulldozer self-localization systems rely on RTK-GNSS (Real Time Kinematic-Global Navigation Sat…
CatCMA with Margin for Single- and Multi-Objective Mixed-Variable Black-Box Optimization
Ryoki Hamano, Masahiro Nomura, Shota Saito +2
This study focuses on mixed-variable black-box optimization (MV-BBO), addressing continuous, integer, and categorical variables. Many real-world MV-BBO problems involve dependencie…
Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers
Rin Ashizawa, Yoichi Hirose, Nozomu Yoshinari +2
Prompt optimization aims to search for effective prompts that enhance the performance of large language models (LLMs). Although existing prompt optimization methods have discovered…
Warm Starting of CMA-ES for Contextual Optimization Problems
Yuta Sekino, Kento Uchida, Shinichi Shirakawa
Several practical applications of evolutionary computation possess objective functions that receive the design variables and externally given parameters. Such problems are termed c…
Covariance Matrix Adaptation Evolution Strategy for Low Effective Dimensionality
Kento Uchida, Teppei Yamaguchi, Shinichi Shirakawa
Despite the state-of-the-art performance of the covariance matrix adaptation evolution strategy (CMA-ES), high-dimensional black-box optimization problems are challenging tasks. Su…
CMA-ES for Discrete and Mixed-Variable Optimization on Sets of Points
Kento Uchida, Ryoki Hamano, Masahiro Nomura +2
Discrete and mixed-variable optimization problems have appeared in several real-world applications. Most of the research on mixed-variable optimization considers a mixture of integ…
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…
CMA-ES with Adaptive Reevaluation for Multiplicative Noise
Kento Uchida, Kenta Nishihara, Shinichi Shirakawa
The covariance matrix adaptation evolution strategy (CMA-ES) is a powerful optimization method for continuous black-box optimization problems. Several noise-handling methods have b…
CMA-ES for Safe Optimization
Kento Uchida, Ryoki Hamano, Masahiro Nomura +2
In several real-world applications in medical and control engineering, there are unsafe solutions whose evaluations involve inherent risk. This optimization setting is known as saf…
CatCMA : Stochastic Optimization for Mixed-Category Problems
Ryoki Hamano, Shota Saito, Masahiro Nomura +2
Black-box optimization problems often require simultaneously optimizing different types of variables, such as continuous, integer, and categorical variables. Unlike integer variabl…
(1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems
Yohei Watanabe, Kento Uchida, Ryoki Hamano +3
The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient continuous black-box optimization method. The CMA-ES possesses many attractive features, including inva…
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari +3
High sensitivity of neural architecture search (NAS) methods against their input such as step-size (i.e., learning rate) and search space prevents practitioners from applying them…