3 papers
stat.ML2025
Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization
Shion Takeno, Yu Inatsu, Masayuki Karasuyama +1
Bayesian optimization is a powerful tool for optimizing an expensive-to-evaluate black-box function. In particular, the effectiveness of expected improvement (EI) has been demonstr…
cs.LG2025
Bayesian Optimization for Simultaneous Selection of Machine Learning Algorithms and Hyperparameters on Shared Latent Space
Kazuki Ishikawa, Ryota Ozaki, Yohei Kanzaki +2
Selecting the optimal combination of a machine learning (ML) algorithm and its hyper-parameters is crucial for the development of high-performance ML systems. However, since the co…
stat.ML2024
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms
Yu Inatsu, Shion Takeno, Kentaro Kutsukake +1
Level set estimation (LSE), the problem of identifying the set of input points where a function takes value above (or below) a given threshold, is important in practical applicatio…