2 citations · 4 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
Multi-Objective Bayesian Optimization with Active Preference Learning
Ryota Ozaki, Kazuki Ishikawa, Youhei Kanzaki +4
There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, iden…
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama +1
Among various acquisition functions (AFs) in Bayesian optimization (BO), Gaussian process upper confidence bound (GP-UCB) and Thompson sampling (TS) are well-known options with est…
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation
Shota Hozumi, Kentaro Kutsukake, Kota Matsui +3
In material characterization, identifying defective areas on a material surface is fundamental. The conventional approach involves measuring the relevant physical properties point-…
Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records
Yusuke Takagi, Noriaki Hashimoto, Hiroki Masuda +4
In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been develope…