3 citations · 5 across the 6 of their papers we have counts for
4 papers · 1 filter
Best-Arm Identification-Based Trust Region Selection for Bayesian Optimization on Multimodal Functions
Nobuo Namura, Sho Takemori
Gaussian process-based Bayesian optimization (BO) is a popular approach for expensive black-box optimization, but its performance often degrades on complex multimodal or high-dimen…
Regional Expected Improvement for Efficient Trust Region Selection in High-Dimensional Bayesian Optimization
Nobuo Namura, Sho Takemori
Real-world optimization problems often involve complex objective functions with costly evaluations. While Bayesian optimization (BO) with Gaussian processes is effective for these…
Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models
Nobuo Namura, Yuma Ichikawa
Recent advancements in time-series anomaly detection have relied on deep learning models to handle the diverse behaviors of time-series data. However, these models often suffer fro…
Surrogate-Assisted Reference Vector Adaptation to Various Pareto Front Shapes for Many-Objective Bayesian Optimization
Nobuo Namura
We propose a surrogate-assisted reference vector adaptation (SRVA) method to solve expensive multi- and many-objective optimization problems with various Pareto front shapes. SRVA…